Current Medicinal Chemistry, 2013, 20, ????-???? 1

0929-8673/13 $58.00+.00 © 2013 Bentham Science Publishers

Virtual Screening Strategies in Drug Discovery: A Critical Review

A. Lavecchia* and C. Di Giovanni

Department of Pharmacy, “Drug Discovery” Laboratory, University of Naples “Federico II”, via D. Montesano 49, I-

80131 Napoli, Italy

Abstract: Virtual screening (VS) is a powerful technique for identifying hit molecules as starting points for medicinal chemistry. The number of methods and softwares which use the ligand and target-based VS approaches is increasing at a rapid pace. What, however, are the real advantages and disadvantages of the VS technology and how applicable is it to drug discovery projects? This review provides a comprehensive appraisal of several VS approaches currently available. In the first part of this work, an overview of the recent progress and advances in both ligand-based VS (LBVS) and structure-based VS (SBVS) strategies highlighting current problems and limitations will be provided. Special emphasis will be given to in silico chemogenomics approaches which utilize annotated ligand-target as well as protein-ligand interaction databases and which could predict or reveal promiscuous binding and polypharmacology, the knowledge of which would help medicinal chemists to design more potent clinical candidates with fewer side effects. In the second part, recent case studies (all published in the last two years) will be discussed where the VS technology has been applied successfully. A critical analysis of these case studies provides a good platform in order to estimate the applicability of various VS strate-gies in the new lead identification and optimization.

Keywords: Drug discovery, structure-based virtual screening, ligand-based virtual screening, chemogenomics, biologically relevant chemical space, docking, computational methods.

INTRODUCTION

The discovery of innovative leads is an expensive and time-consuming process. It is estimated that a typical drug discovery cycle, from lead identification through to clinical trials, can take 14 years [1] with a cost of 800 million US dollars. In the early 1990s, rapid developments in the fields of combinatorial chemistry and high-throughput screening (HTS) technologies offered great promise for accelerating the drug discovery process by enabling huge libraries of compounds to be synthesized and screened in short periods of time. Many of the identified hits, however, failed in the lead optimization process due to absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) deficien-cies. It was therefore necessary to develop alternative strate-gies that could help to choose an appropriate set of com-pounds, removing thereby the unsuitable structures, and to limit the use of a significant amount of resources. In this context, the identification of hits by computational methods such as virtual screening (VS) can represent a crucial step in early-stage drug discovery. The first time the term “virtual screening” appeared in a peer-reviewed publication was in 1997. Since then the field of VS has become more and more popular and has experienced rapid growth in pharmaceutical research. (Fig. 1) and (Table 1) show the number of VS pub-lications from 2000 to the present in journals with impact factors of at least 2. The data comes from a search of the

*Address correspondence to this author at the Department of Pharmacy, “Drug Discovery” Laboratory, University of Naples “Federico II”, via D. Montesano 49, I-80131 Napoli, Italy; Tel: +39-081-678613; Fax: +39-081-678012; E-mail: lavecchi@unina.it.

American Chemical Society's SciFinder Search engine using “virtual screening” as the key word.

VS is a detailed, knowledge-driven, compound database searching approach that attempts to find novel compounds and chemotypes with a required biological activity as alter-natives to existing ligands or sometimes to make first inroads into finding ligands for unexplored putative drug targets for which crystal structures, solution structures or high confi-dence homology models are available. VS is usually de-scribed as a step by step method with a cascade of sequential filters able to narrow down and choose a set of lead-like hits with potential biological activity against intended drug tar-gets. The compounds studied do not necessarily exist, and their "testing" does not consume valuable substance material. Taken to its extreme, any molecule can, in theory, be evalu-ated by VS. Depending on the intended follow-up, databases for VS contain up to 10 million available compounds and this number can be handled in a VS experiment. Such com-pounds can be obtained either from a multitude of external sources such as compound libraries from commercial ven-dors or from public or commercial databases. This approach can be used in more and more new and conceptually diverse ways. VS can be divided into two broad categories, namely ligand-based (LBVS) and structure-based (SBVS) [2]. LBVS strategies utilize structure-activity data from a set of known actives in order to identify candidate compounds for experi-mental evaluation [3]. LBVS methods include approaches such as similarity and substructure searching, quantitative structure-activity relationships (QSAR), and pharmacophore- and three-dimensional shape matching [4]. SBVS, on the other hand, utilizes the three-dimensional (3D) structure of

2 Current Medicinal Chemistry, 2013, Vol. 20, No. 1 Lavecchia and Di Giovanni

the biological target (determined either experimentally through X-ray crystallography or NMR or computationally through homology modeling) to dock the candidate mole-cules and rank them based on their predicted binding affinity or complementarity to the binding site.

Fig. (1). Total number of VS studies appearing between 2000 and 2012 in 12 journals with impact factor > 2.

Table 1. Virtual Screening Studies Published from 2000 to

Present For Each Journala

Journal No of Publications

Journal of Chemical Information and Modeling 438

Journal of Medicinal Chemistry 316

Bioorganic & Medicinal Chemistry Letters 196

Journal of Computer-Aided Molecular Design 151

Bioorganic & Medicinal Chemistry 145

ChemMedChem 92

European Journal of Medicinal Chemistry 84

Chemical Biology & Drug Design 77

ACS Chemical Biology 13

ChemBioChem 11

Nature Chemical Biology 8

Angewandte Chemie 2

a Journals are ranked by the total number of VS publications.

The first part of this review discusses some of the broad

issues that have become apparent with the application of both SBVS and LBVS in drug discovery in the past decade and considers how the field might be advanced. The second half of the review will focus on a series of recent VS case studies which are indented to illustrate the range of possible targets in VS, to provide an account of inclusive methodol-ogy and to reveal the expectations for realistic goals.

SMALL MOLECULE DATABASE MANAGEMENT

The first requirement in conventional drug discovery is the identification of a valid target, i.e. a molecule which has

a link with the disease under scrutiny such that pharmacol-ogical intervention would be expected to cure the disease or ameliorate its symptoms. This first step consists of identify-ing potential targets and validating them as actual targets. Identification of potential targets requires exploration of Bio-logical Space (Fig. 2) through the sequencing of the human genome, with their reliance on high throughput sequencing technologies and computing algorithms to handle and ana-lyze the large volumes of data being generated. Having iden-tified and validated a biological target, the next step is to identify an entity which can specifically interact with that target in such a way as to produce a therapeutic effect. In classical drug discovery, the ‘entity’ is a small molecule chemical compound. Identification of a chemical compound which specifically binds to the right part of a target protein is not easy. The chances of success are increased by exploring the Chemical Space as fully as possible. The total theoretical Chemical Space, i.e. the number of different compounds that could in theory be synthesised [5] is estimated to be in the region of 1060 [6, 7]. This is a number so large that it is not readily comprehensible; as far as our current capabilities are concerned, it might as well be infinity.

Fig. (2). Schematic representation of the goal of enhancement of screening collections for pharmaceutical research. Chemical Space represents the immense universe of possible molecules. Biological Space represents all those molecules, known and unknown, which bind effectively to any biological target. Drug Space is only a small region at the intersection of the Medicinal Chemistry Space (small molecules ever made) with the Biological Space, as it only includes those bioactive small molecules that are bioavailable, safe and effi-cacious in treating disease. VS has the unique opportunity to ex-pand into unexplored chemical space in order to find new pockets of space where drugs are likely to be discovered

Although there are efforts underway to make very large libraries, complete coverage of Chemical Space appears to be beyond current capabilities, and it is unusual for any given pharmaceutical group to have a library of more than one or two million. Moreover, even the largest screening campaigns are limited to ~106 compounds, a practically in-finitesimal fraction of the entire space. Fortunately, however, only a small portion of that space can be expected to com-prise molecules that are stable and soluble in aqueous media, have appropriate functional groups to interact with biological targets such as proteins and nucleic acids and have sufficient structural complexity [8] to do so with useful levels of speci-

Virtual Screening Strategies in Drug Discovery: A Critical Review Current Medicinal Chemistry, 2013, Vol. 20, No. 1 3

ficity (Medicinal Chemistry Space). It is assumed that the Chemical Space represented by traditional screening collec-tions is inadequate to successfully tackle promising un-validated or un-druggable biological targets, and new regions of Chemical Space need to be explored. Possible sources of molecules with atypical molecular scaffolds accessing such unexplored regions of space can be derived from natural products, molecules derived mainly from microbes, plants or marine organisms or through emerging technologies such as diversity-oriented synthesis for generating natural product-like combinatorial libraries. Notably, despite the tremendous impact that natural products have historically had on drug discovery [9], there are substantial differences between the structures of synthetic drugs and natural products [10].

Table 2. Properties Typically Used for Lead-likeness Criteria

[11]

Properties Lead-likeness

Molecular Weight (MW) 200-460

Lipophilicity (CLogP) -4/4.2

H-bond donor (sum of NH and OH) 5

H-bond acceptor (sum of N and O) 9

Polar Surface Area (PSA) 170 Å2

Number of rotatable bonds 10

CACO-2 membrane permeability 100

Solubility in water (logS) -5/0.5

Others Absence of both toxic and

reactive fragments

The concept of drug-likeness has been introduced to de-termine the characteristics necessary for a drug to be suc-cessful. So, over time, more stringent rules and guidelines for lead- and drug-like features have been applied for com-pounds in a screening library. (Table 2) lists the lead-like guidelines suggested by Hann and Oprea [11].

Many in silico tools can be used to design libraries [4] of compounds with drug-like properties. These are predominantly biophysical properties based on empirical rules. A well-known example is Lipinski's "Rule of Five" [12] which states that a compound is likely to be "non-drug-like" if it has more than five hydrogen bond donors, more than 10 hydrogen bond acceptors, molecular mass is greater than 500 and lipophilicity is above 5. This rule has been re-cently revisited using pharmacokinetic data in rats [13]. Many related rules have been subsequently modified and proposed as the "Rule-of-Three" [14], which defines frag-ment properties with an average molecular weight 300 Da, a Clog P 3, the number of hydrogen bond donors 3, the number of hydrogen bond acceptors 3, and the number of rotatable bonds < 3. Recently, Pfizer's "Rule of 3/75" has been described which states that compounds with a calculated partition coefficient (ClogP) of < 3 and topological polar surface area (TPSA) > 75 have the best chances of being well tolerated from a safety perspective in vivo [15]. (Table 3) lists several libraries having drug-like properties compliant with Rule of 3 or 5.

As well as traditional physicochemical property filters, there are now a number of flags for more complex properties [16]. For example, when preparing a collection for VS, it is appropriate to remove molecules that contain chemically reactive groups or other undesirable functionalities [17-23] that interfere with the screening detection techniques and cause them to elicit a positive signal. Compounds may be considered less attractive due to the presence of toxicophores (e.g. nitro, aniline, hydantoin, alkyl halide peroxide, and car-

Table 3. Small Molecule Databases Compliant with “Rule of Three” or "Rule of Five"

Virtual Library Rule Name URL

Vitas-M Allium Library 3 http://www.vitasmlab.com/

TimTec Fragment-Based Library 3 and 5 http://www.timtec.net/

ChemBridge Fragment Library 3 http://www.chembridge.com/

Lifechemicals General Fragments Library 3 http://www.lifechemicals.com/

ASINEX's BioFragments 3 http://www.asinex.com/

Enamine Fragment Library 3 http://www.enamine.net/

Keyorganics BIONET Fragment Library 3 http://www.keyorganics.co.uk/

Maybridge Ro3 Library 3 http://www.maybridge.com/

Maybridge Screening Collection 5 http://www.maybridge.com/

OTAVA Fragment Library 3 http://www.otavachemicals.com/

Prestwick Fragment Library 3 http://www.prestwickchemical.com/

ChemDiv Fragment Based Library 3 http://us.chemdiv.com/

4 Current Medicinal Chemistry, 2013, Vol. 20, No. 1 Lavecchia and Di Giovanni

bazide), which are associated with metabolism-mediated toxicity. Alternatively, groups such as aldehydes and epox-ides may be considered inappropriately electrophilic, whereas others such as thiols are redox active. Unsuitable leads may include crown ethers, disulfide, and several natu-ral scaffolds such as quinones, polyenes, and cyclohexi-midine derivatives. Further problems concern some com-pounds that may be autofluorescent and others that may ag-gregate [24] at certain concentrations and produce false posi-tives in some assays. Aggregator compounds are difficult to predict computationally and require additional biophysical methods (e.g., light scattering experiments) or modifications of the biochemical assay (e.g., addition of detergent or pro-tein serum) to support their detection experimentally [25]. In short, these “bad” molecules encompass chemically reactive, assay-interfering compounds and are often referred to as PAINS (pan-assay interfering substances) [26] or frequent hitters [26]. Computationally, the elimination of reactive and warhead-containing compounds can be accomplished by applying various sets of substructure filters [27, 28]. Fre-quent hitters can be identified by statistical models or other VS methods [29]. There are also compounds that interfere with the readout of a particular assay type, such as kinase inhibitors that cause false positive readouts in reporter-gene assays [30]. Other unsuitable groups are detected by the ALARM NMR protocol [31, 32] Moreover, many of these reactive or unsuitable groups can be flagged by the recent FAF-Drugs2 online server [33], a free ADME/tox filtering tool to assist drug discovery and chemical biology projects.

VS is also affected by high-energy or physically unrealis-tic conformations of molecules. Some conformational sam-pling methods do not employ energy minimization to refine and properly rank the resulting geometries, thus providing high-energy conformations. If such geometries are not elimi-nated, either when the database is constructed or when the virtual hits are identified, the resulting output could contain many false positives.

Another relevant question concerns the diversity of a compound collection. Of course, answering this question implies that the concept of molecular diversity [34] has pre-viously been addressed; this is not within the scope of the present review. Here, the issue of diversity will be addressed from a medicinal chemistry point of view, i.e. how many substructures (scaffolds) amenable to fast library design are present in the library? If it is assumed that shape recognition is a very important event in ligand binding, then enrichment in different scaffolds should be a key factor in the evaluation of the quality of a library. Diversity of chemical databases is evaluated by examining how much the compounds within the library differ in terms of the distribution of their properties. Different kinds of diversity quantification are carried out: distance-based methods, cell-partitioning methods and clustering methods. The study by Krier et al. provides a view of the diverse nature of the databases and helps in selecting an appropriate chemical database [35]. Therefore, the choice of screening collection(s) is a key fac-tor to the success of the screening process. It should be as-certained whether the Chemical Space covered by a single or several libraries overlaps the target space that is being screened. It is therefore strongly advisable to identify non-

redundant scaffolds among all available libraries in order to customize its screening database. As most of these collec-tions are quite dynamic entities, it is also advisable to up-grade their stock in order to reflect recent changes in indi-vidual collections.

Chemical databases and compound collections are often freely available from vendors or institutions [4, 36-39]. They include known drugs, carbohydrates, synthetic, natural and targeted compounds (Table 4) [40-47]. ZINC [40] is a free database of commercially available compounds that in its current version contains over 13 million purchasable com-pounds from vendor catalogs annotated with biologically relevant properties (molecular weight, calculated Log P and number of rotatable bonds). Subsets of drug-like, lead-like, and fragment compounds have been generated and can be accessed separately.

Similarly, ChemDB [41] is a searchable chemical database containing nearly 5 million small molecules with their stereoisomers collected from electronic catalogs of commercial vendors. Little more than five years old, the Chemical Structure Lookup Service now claims 46 million unique structures, followed by ChemSpider with a collection of 20 million compounds, and eMolecules with 10 million. In addition, PubChem is the NCBI public informatics backbone for the NIH Molecular Libraries Initiative focusing on small molecules as systems biology probes and potential therapeutic agents. It consists of PubChem Compound (unique structures), PubChem BioAssay (assay results), and PubChem Substance (samples supplied by depositors). The dynamically growing primary databases contain over 61 mil-lion records of chemical substances, 25 million unique com-pound structures and bioactivity data from more than 1,600 assays. Other publicly available compound databases that contain detailed biological information and data regarding many chemicals and drugs are ChemBank [42] and Drug-Bank [43, 44]. The National Cancer Institute (NCI) Open Database contains ~265,000 freely available structures with 3D coordinates, some ADME proprieties and information about predicted activity. In addition, the PubChem version of the NCI database contains ~15,000 additional structure re-cords not present in the NCI Open Database. NCI also offers four sets of compounds with different characteristics: i) the NCI Natural Products Set II, that contain 120 natural prod-ucts derived from plants and microbes; ii) the NCI Mecha-nistic Set, including a small library of 879 compounds with growth inhibition for cancer cell lines, useful for screening on different targets to discover novel activity; iii) the NCI Diversity Set, which is a restricted collection of 2,000 com-pounds with some lead-like properties such as 5 or fewer rotatable bonds, 1 or less chiral centers and pharmacologi-cally desirable features (i.e., they are not electrophilic, un-stable, organometallic, polycyclic aromatic hydrocarbons, etc.).

The French national chemical library of the Chimio-thèque Nationale has 48,370 synthetic and natural com-pounds available thanks to a material transfer agreement and a small financial contribution. The St. Louis Compound Col-lection contains 60 small molecules (a number which is growing) useful for screening campaigns in the field of

Virtual Screening Strategies in Drug Discovery: A Critical Review Current Medicinal Chemistry, 2013, Vol. 20, No. 1 5

Table 4. Small Molecule Databases and Compound Collections Available from Vendors or Institutions

Database Type No. Cpds

Website

ZINC [40] Public 13 million http://zinc.docking.org

ChemDB [41] Public 5 million http://cdb.ics.uci.edu

eMolecules Commercial 7 million http://www.emolecules.com

ChemSpider Public 26 million http://www.chemspider.com

Pubchem Public 30 million http://pubchem.ncbi.nlm.nih.gov

ChemBank [42] Public 1,2 million http://chembank.broadinstitute.org

DrugBank [43, 44] Public 4,800 drugs; 2,500

targets http://www.drugbank.ca

NCI Open Database Public 265,000 http://cactus.nci.nih.gov/ncidb2.2/

Chimiothèque Nationale Commercial 48,370 http://chimiotheque-nationale.enscm.fr/?lang=fr

Drug Discovery Center Collection Commercial 340,000 http://www.drugdiscovery.uc.edu/

ChEMBL [45] Public 1 million http://www.ebi.ac.uk/chembldb/index.php

WOMBAT [46] Commercial 263,000 http://www.sunsetmolecular.com

ChemBridge Commercial 700,000 http://www.chembridge.com

Specs Commercial 240,000 http://www.specs.net

CoCoCo [47] Public 7 million http://cococo.unimore.it/tiki-index.php

Asinex Commercial 550,000 http://www.asinex.com

Enamine Commercial 1,7 million http://www.enammine.net

Maybridge Commercial 56,000 http://www.maybridge.com

ChemDiv Commercial 1,5 million http://www.chemdiv.com

ACD Commercial 3,9 million http://accelrys.com/products/databases/sourcing/avaible-chemicals-directory.html

MDDR Commercial 150,000 http://accelerys.com/products/databases/bioactivity/mddr.html

infectious diseases available from NCI. The Drug Discovery Center Collection of the University of Cincinnati (UC Com-pound Library) has ~340,000 structurally diverse compounds with drug-like proprieties which are commercially available. (Table 5) lists other targeted compounds collections derived from different vendors.

Several commercial databases also contain compounds capturing information from literature and patent sources. ChEMBL is a large collection of chemicals extracted from literature, including target and bioactivity information for 50,0000 compounds [45]. The World of Molecular Bio AcTivity (WOMBAT) is a commercial database that includes specific links between 300,000 compounds and sequence identifiers for the proteins against which these compounds have been shown to be active [46]. Now, more than 60% of new compounds entering the Chemical Abstract Service (CAS) registry come from patents.

STRUCTURE-BASED AND LIGAND-BASED VIR-

TUAL SCREENING APPROACHES

The application of VS for hit and lead identification fol-lows a typical sequence of processes. Depending on the in-

formation obtainable about the target and/or existing ligands at the beginning of the screening campaign, VS can be tradi-tionally divided into two main approaches: SBVS and LBVS. For the first approach, the protein structure of interest is available and a compound library of small molecules (available via purchase or synthesis) is explored by docking into the active site of the biochemical target using computer algorithms and scoring functions. A mathematical algorithm (referred to as “scoring function”) is then used to evaluate the binding tightness between the docked compound and the target. This is often followed by a post-processing step in which compounds are ranked and selected on the basis of calculated binding scores and/or other criteria. Usually only a few top-ranked compounds are selected as candidates for further experimental assays. Today, there are a number of docking programs commercially (or freely) available with different conformational sampling algorithms and a variety of scoring functions [48] reported in (Table 6) [49-64].

For the second approach, biological data are explored in order to identify known active or inactive compounds that will be used to retrieve other potentially active molecular scaffolds based on similarity measures, common

6 Current Medicinal Chemistry, 2013, Vol. 20, No. 1 Lavecchia and Di Giovanni

Table 5. Targeted Small Molecules Databases from Commercial Vendors

Company Library Name Link Address

Asinex Antibacterials http://www.asinex.com

SPECS Kinase-targeted Library http://www.specs.net/

GPCR Ligands

Kinase Modulators

Protease Inhibitors

Potassium Channels Modulators

Timtec

Nuclear Receptors Ligands

http://www.timtec.net

Kinase-Biased Sets

GPCR Library ChemBridge

Channel-Biased Sets

http://www.chembridge.com

GPCRs ChemDiv

Kinases http://www.chemdiv.com/main.phtml

IBS High-Hit Databases

Analgesics

Antibacterials

Antidiabetics

Cancerostatics

InterBioScreen

Cns regulators

http://www.ibscreen.com

MayBridge http://www.maybridge.com

Bionet

Antimalarial Agents

Active Compounds for Cancer Research Key Organics

Active Compounds for CNS Research

http://www.keyorganics.ltd.uk

GPCR Library

Kinase Library Life Chemicals

Anticancer Library

http://lifechemicals.emolecules.com/

Table 6. Example of Commonly Used Docking Softwares

Software Free for Academia Website

AUTODOCK [49] Yes http://autodock.scripps.edu/

DOCK [50] Yes http://dock.compbio.ucsf.edu/

FlexX [51] No http://www.biosolveit.de/flexx/

GLIDE [52] No http://www.schrodinger.com/

GOLD [53] No http://www.ccdc.cam.ac.uk/products/life_sciences/gold/

EADock [54] http://lausanne.isb-sib.ch/~agrosdid/projects/eadock/eadock_dss.php

Virtual Screening Strategies in Drug Discovery: A Critical Review Current Medicinal Chemistry, 2013, Vol. 20, No. 1 7

(Table 6) contd….

Software Free for Academia Website

Surflex [55] No http://www.tripos.com/index.php

ICM [56] No http://www.molsoft.com/docking.html

LigandFit [57] No http://accelrys.com/products/discovery-studio

eHiTS [58] No http://www.simbiosys.ca/ehits/index.html

SLIDE [59] Yes on demand http://www.bch.msu.edu/~kuhn/software/slide/index.html

ROSETTA_DOCK [60] Yes on demand http://rosettadock.graylab.jhu.edu/

Virtual Docker [61] No http://www.molegro.com/mvd-product.php

Ligand-Receptor Docking [62] No http://www.chemcomp.com/software-sbd.htm

FRED [63] Yes on demand http://www.eyesopen.com/oedocking

ZDOCK [64] Yes http://zlab.umassmed.edu/zdock/

pharmacophores or descriptor values. Depending on the available information, both methods can be used individually or in combination. None of these proposed approaches can be a priori considered superior to the other because, as sug-gested in the literature, depending on the case the one can outperform the other.

A critical step for the success of SBVS is the scoring of the ligands [65, 66]. Although prediction of the ligand-binding pose is usually possible with the available methods, scoring is still very challenging and it is thus difficult to identify the correct binding pose or to rank compounds. Some of the difficulties with scoring functions come from the fact that several terms and events, for example some mo-lecular interactions, are difficult to parameterize. Scoring functions are used a) to evaluate different bound poses for a single ligand generated by the docking algorithm in order to select the energetically preferred pose; b) to rank different docked ligands in order to discriminate the active com-pounds. Scoring functions developed during the last few years have been reviewed in other works [67, 68]. Some of the commonly used scoring functions can be grouped into three broad categories: force field-based, knowledge-based and empirical [69, 70]. Some scoring functions include mixed force field and empirical terms. The force field score functions [71, 72] estimate the binding free energy as a sum of independent molecular mechanics force fields potentials, such as Coulomb, van der Waals, hydrogen bonding. Solva-tion [73, 74] and entropy [75] contributions can also be con-sidered.

The empirical scoring functions [76, 77] represent the binding free energy as a weighted sum of interaction terms like hydrogen bonding and hydrophobic contacts by fitting the scoring function to experimental binding affinity data for a training set of protein-ligand complexes. These functions have proven to be successful for many protein-ligand com-plexes [78]. The knowledge-based scoring functions [79, 80] are exclusively derived from statistical analyses of atom-pairs frequencies in protein-ligand complexes with known 3D structures.

Over the past two decades, significant effort has been put into refining the scoring functions to accurately predict the binding free energies, at least in the relative sense, so they can be used for rank ordering if not for quantitative measures of activities. Nevertheless, given the complexity of the ligand-protein binding process and the approximations made in calculating desolvation and entropic terms, the docking scores have not proven accurate in predicting binding affini-ties [81-83]. Strategies proposed to improve the performance of current scoring functions include adding other factors to account for solvation and entropic effects [80] deriving more accurate energy terms by means of high-level quantum cal-culations [84], target-specific scoring functions [85] and con-sensus scoring by combination of multiple scoring functions [86, 87]. On the other hand, based on our experience, it is more productive if docking scores are treated as a general guide to goodness-of-fit and combined with more accurate measures of the tightness-of-fit by specific molecular pa-rameters that reflect the essence of the binding event. Such parameters could be derived from monitoring the critical hydrogen bonds which are important for the study, the ge-ometry of an essential - stacking and/or the occupation of a hydrophobic pocket that pre-positions the ligand in the binding site.

Another unexploited aspect of SBVS is the target struc-tural flexibility [88], mainly because of the computational cost and complexity required to model it properly. In the recent years, the greatest challenges of many docking algo-rithms are directed towards properly considering target flexi-bility. Soft docking (available in all docking programs), or the softening of van der Waals potentials can allow for small overlaps between the ligand and receptor without large steric penalties [89]. However, this may increase the rate of false positives because more diverse structures are allowed to bind. It also does not allow for larger conformational changes like side-chain rotations or protein backbone mo-tions. Several docking programs including AUTODOCK4 [49], DOCK [50], GOLD [53], EADock [54], IFREDA [56], FlexE [90], or GLIDE induced Fit [91] (Table 7) allow rota-tion around torsional degrees of freedom of selected side

8 Current Medicinal Chemistry, 2013, Vol. 20, No. 1 Lavecchia and Di Giovanni

chains (e.g. those of the binding site) adopting the same methods used to explore the conformational space of the flexible ligand.

Many other theoretical approaches are in constant devel-opment and their applications are particularly interesting for VS experiments. One of these approaches is the Relaxed Complex Scheme (RCS). The RCS uses an ensemble of low energy structures extracted from MD simulations to search ligand libraries via small molecule docking [92, 93]. It com-bines the advantages of docking algorithms with dynamic structural information provided by MD simulations, explic-itly accounting for the flexibility of both the receptor and docked ligands. Increasingly, longer time scale MD simulations enhance the ability to effectively sample the receptor conformational space prior to docking. This scheme has been developed in combination with various MD soft-ware packages (including AMBER [94], NAMD [95], GROMACS [96] and AUTODOCK for ligand docking [49].

When a set of active ligands is available, it is possible to compute their shared pharmacophore. A pharmacophore is defined as the 3D arrangement of features that is crucial for a ligand molecule in order to interact with a target receptor in a specific binding site. Once identified, a pharmacophore can serve as an important model for VS, especially in case where the 3D structure of the receptor is unknown and docking techniques are not applicable. The strength of pharma-cophore-based screening compared to other ligand similarity screening approaches lies in the ability to detect a diverse set of putative active compounds with totally different chemical scaffolds. This increases the chances that some of the de-tected compounds will pass all the stages of drug develop-ment. Besides screening, pharmacophore is also a powerful model in other applications of drug development, like de novo design, lead optimization, ADME/Tox studies and Chemogenomics [97, 98]. However, pharmacophore-based screening has specific drawbacks such as insufficient or in-accurate conformational sampling and molecular overlay, ambiguities in pharmacophore pattern (mainly due to uncer-tainty related to the protonation/tautomeric status of com-pounds), selection of improper anchoring points in active sites when ligand cocrystals structures are not available and incorrect binding affinity estimation [99].

Additionally, one of the most popular ligand-based drug design approaches is quantitative structure-activity relation-ships (QSAR). The aim of QSAR is to determine the rela-tionship between structural/physicochemical properties of active compounds to their biological activity [100-102]. Knowledge of the activity levels of the compounds, such as binding affinity (KD) or inhibitory concentration (IC50), is requisite for QSAR methods. Molecular structures are nor-mally represented by specific sets of structural and physico-chemical properties (molecular descriptors) of the molecules considered most relevant to their binding activities. The quality of QSAR models is affected by compatibility of con-cepts, representativeness of structure-activity data, selection of molecular descriptors, influence of data outliers, fitness of developed quantitative relationships, starting geometry in 3D-QSAR and multiple choices of solutions [103].

One of the simplest and most widely used techniques of VS is the similarity searching, in which a known bioactive reference structure is searched against a database to identify the nearest-neighbour molecules since these are the most likely to exhibit the bioactivity of interest [104-108]. The analysis can be done by topological indices, molecular fin-gerprints, fragment or substructure based descriptors. Some examples of the common molecular fingerprints based on 2D or 3D descriptors are represented by MOLPRINT 2D [109], Property Descriptor value Range-derived FingerPrint (PDR-FP) [110], Rapid Overlay of Chemical Structures (ROCS) program [111] and shape fingerprints [112]. A new applica-tion of the similarity analysis is based on the use of mapping methods. Mapping algorithms originated with the introduc-tion of dynamic mapping of consensus positions (DMC) de-termine and iteratively transform consensus positions of classes of active compounds in simplified descriptor spaces of gradually increasing dimensionality. Database compounds that match these positions are selected as candidates for hit identification [113, 114]. Similarity searching method is highly effective and fast, often producing superior VS per-formance compared to docking [115], but tends to be limited by the requirement of structural or sub-structural similarity to the known active compounds.

Machine Learning is quickly gaining popularity in LBVS as novel algorithms are proposed to build accurate and robust

Table 7. Docking Programs That Include Protein Flexibility

Program and Ref. Ligand Flexibility Protein Flexibility Scoring function

AUTODOCK4 [49] Evolutionary algorithm Flexible side chain Force field

DOCK [50] Incremental build Protein side chain and flexibility Force field or contact score

GOLD [53] Evolutionary algorithm Protein side chain and backbone

flexibility Empirical score

EADock [54] Evolutionary algorithm Flexible side chain and backbone Force field

ICM, IFREDA [56] Pseudo-Brownian sampling and

local minimization Flexible side chains Force field and Empirical score

FlexE [90] Incremental build Ensemble of protein structure Empirical score

GLIDE Induced Fit [91] Exhaustive search Flexible side chains Empirical score

Virtual Screening Strategies in Drug Discovery: A Critical Review Current Medicinal Chemistry, 2013, Vol. 20, No. 1 9

quantitative structure-activity relationships. Different tech-niques are proposed and each method has its own advantages and disadvantages. Among these methods, regression and classification methods such as Multiple Linear Regression, Nearest Neighbors, Naïve Bayesian Classification, Support Vector Machines, Neural Networks and Decision Trees have been successfully applied. These methods, reviewed in a recent publication by Melville et al. [116], are relatively data hungry as many active and inactive compounds must be used to train and subsequently test the models. These models cap-ture the properties discriminating active molecules from in-active molecules in order assess novel small molecules for their likelihood of interacting with the target of interest.

The performance of machine learning methods depends on various factors such as training set diversity, their ability to deal with imbalanced datasets (inactive compounds typi-cally outnumber active compounds), and parameter ranges in covering active and inactive chemical space [117]. Machine learning models capable of screening large compound data-bases with good yields of actives and low false-hit rates can be developed by using highly diverse training datasets com-posed of predicted inactive compounds [118-120].

COMBINING LIGAND AND STRUCTURE-BASED

APPROACHES

In recent years there has been increasing interest in com-bining SBVS methods with LBVS ones which, historically, have enjoyed more application and success than SBVS [121]. Such a unified approach would exploit all available structural and chemical information in the search for new molecules and holds significant potential. It is accessible only when both 3D structures of the target protein (X-ray, NMR, comparative modeling) and known active compounds are available and a number of attempts have been made to do so [122-128]. In most of these cases, SBVS and LBVS techniques are com-bined in a sequential or parallel manner. The serial combina-tion is designed as a hierarchical procedure with, in general, the fastest and less refined approaches as the first step, and the most computational expensive techniques as the second step. The parallel combination aims at comparing the selected com-pounds of both methods either as a complementary selection (top ranked compounds from each methods) or a consensual selection (compounds selected by both approaches). Unfortu-nately, the reports regarding attempts to fuse SBVS and LBVS approaches have shown mixed results [129]. This is due at least in part to the inability of common fusion strategies to systematically deliver superior performance. Alternative ap-proaches involve either preselecting or rescoring the structure-based docking library using ligand-based methods [130]. In either case, the fusion of a “good” algorithm with those of a “bad” algorithm (in terms of absolute performance against that particular target) tends to yield the “average” result, and cur-rently there is no approach which permits distinguishing the good from the bad.

IN SILICO CHEMOGENOMICS APPROACHES

The Human Genome Project [131] and the birth of ac-companying technology has made a breakthrough regarding future medicinal chemistry. Sequences, identified thanks to the Human Genome Project and structure databases in com-

bination with knowledge from medicinal chemistry allowed the establishment of a new approach called chemogenomics [132]. Chemogenomics focuses on the exploration of target gene families. Small molecule leads, indentified by their ability to interact with a single member of a gene family, are used in studying the biological role of another gene family member with unknown function. The remaining members are usually specified by sequence homology [133].

Chemogenomics aims to systematically study the bio-logical effect of a large number of small molecules (ligands) on a large number of macromolecular targets (gene products) [133-136]. Since experimental measurements of a large number of ligand–target interactions are time-consuming and cost intensive, they are often complemented by high-throughput in silico chemogenomic approaches. Typically, these VS methods are divided into ligand-based and target-based approaches, and both approaches can be employed to profile either a ligand against a set of proteins or a set of ligands against one specific protein target [137, 138]. Both methods have been successfully applied in the drug discov-ery context [137, 138] with the aim of guiding the develop-ment of a compound with the desired bioactivity profile [139-142].

It should be noted that the applicability of in silico che-mogenomics models depends on the quality and complete-ness of the training sets that are used for model construction and validation [143]. Bioactivity data of small molecules in particular are often incomplete since molecules are not usu-ally screened systematically against a large panel of protein targets but often only on a select set comprising the target of interest and a limited number of proteins over which selec-tivity needs to be achieved [144]. Furthermore, most scien-tific studies focused on the presentation of “active” mole-cules and (potentially) “inactive” molecules are often not reported, leading to a selection bias of published bioactivity data [144]. Even in annotated ligand databases such as ChEMBLdb, [45] DrugBank [43, 44], BindingDB [145-147], PDBBind [148], MOAD [149, 150], WOMBAT [46], and GLIDA [151] protein–ligand interaction matrices are incom-plete [142].

Computational methods have, however, been success-fully used to fill the gap in experimental ligand-target affin-ity matrices [152] and to identify new drug–target associa-tions [137]. From generalization of this analysis, the high number of active molecules in target-annotated chemical databases even allows for the identification of molecular features that determine binding to specific proteins and pro-tein classes [153]. The principle that similar receptors bind similar ligands [154] is used in LBVS methods that extrapo-late from known active compounds [155] and aim to identify structurally diverse compounds having similar bioactivities [156] by use of techniques like substructure mining [153], molecular fingerprint similarity searches, [157] and ligand-based pharmacophore models [158]. These techniques are generally faster than methods utilizing the structure of the protein target, such as molecular docking [159] and protein structure-based pharmacophore models [160] and, in most cases, they can be trained on larger data sets. Structure-based methods, on the other hand, are more suitable for finding

10 Current Medicinal Chemistry, 2013, Vol. 20, No. 1 Lavecchia and Di Giovanni

ligands that are structurally novel and offer insight in the atomic details of protein-ligand interactions.

If chemogenomics works reliably, it is very attractive for pharmaceutical research. Even in an early phase of drug re-search, the prediction of bioprofiles could indicate potential off-targets of a compound which may cause unwanted side effects. The similarity of proteins can help to identify chemi-cal starting points or tools for the investigation of new tar-gets. It was recently observed that many if not even all drugs bind to several targets (called polypharmacology) which has a clear relevance for the therapeutic action of the drugs and/or for the side effects [161]. The target spectrum of a drug is essential information for drug re-purposing where a known drug is used to treat a different disease. A better in-sight into the disease-relevant targets and pathways leading to better therapeutic approaches are further benefits of che-mogenomics applications [162]. Last but not least in pheno-typic screening, the bioprofile of an active compound may help to identify the relevant target(s) [163].

RECENT VIRTUAL SCREENING CASE STUDIES

The number of applications of VS has been rapidly mounting in recent years. In this section, we report twenty-three successful VS studies published in the last two years in several journals in the field of medicinal chemistry and drug design. These representative cases have been grouped into three categories based on the potencies (IC50, EC50, Ki, or Kd) of the VS hits identified, i.e. < 1 μM, between 1-10 μM and between 10-100 μM respectively. (Tables 9, 10 and 11) offer a summary of the key features of each case study.

Case Studies Reporting Hits with Potency < 1 μM

In 2011 Xing et al. [164] published the discovery of po-tent inhibitors of soluble epoxide hydrolase as potential agents in the treatment of cardiovascular dysfunction and in the prevention of renal damage. In their paper, the authors described how a SBVS was successfully applied to the de-sign of combinatorial libraries based on a benzoxazole template to discover novel and potent soluble epoxide hy-drolase inhibitors. Critical ligand-protein hydrogen bond formation of the modeled binary complexes was utilized as major criteria of selection. By exploitation of the favorable binding elements, two iterations of library design based on amide coupling were employed, guided principally by the docking results of the enumerated virtual products. Of the 383 final compounds synthetically delivered, 90% were found active in the soluble epoxide hydrolase enzyme as-say, thus corroborating the high success rate of the design. Subsequent IC50 determination confirmed 23 compounds as single digit nanomolar soluble epoxide hydrolase inhibi-tors. The most active compound 1 (Chart (1) and (Table 8) showed an IC50 of 3.6 nM. A retrospective analysis showed that docking scores computed by five different scoring functions (FlexX, DOCK, GOLD, ChemScore, and PMF) resulted less effective at differentiating good binders from poor ones than the hydrogen bond criteria used in the de-sign. These results, once again, reveal the deficiency of scoring functions as they are designed to be rapidly evalu-ated and applicable across heterogeneous receptors and ligands.

S N

N N

O

O

O

O

N

Cl

NH

O

1

IC50 = 3.6 nM

N

O

OH

O

N

S

OH

5

IC50 = 44 nM

N

O NH

NH

O

NH

O

F3C Cl

6

Ki 5-HT2A = 1959 nM

Ki 5-HT2B = 56 nM

Ki 5-HT2C = 417 nM

N

N

O CN

O

7

IC50 = 200-890 nM

NH

O

8

IC50 = 78 nM

2

IC50 = 9.4 nM

HN

HN

O

CF3

Cl

O

Cl

9

IC50 = 400 nM

HO3S

O

COOH

O

OHOH

HO

10

IC50 Cdc25A = 240 nM

IC50 Cdc25B = 100 nM

OH

N

S

O

COOH

O

N

N

N

N

O3

Ki = 65.3 nM

O

4

Ki = 75.3 nM

OH

N

S

O

COOH

O

N

N

N

N

O

O

Chart (1). Chemical structures of hits with potency < 1 μM.

Virtual Screening Strategies in Drug Discovery: A Critical Review Current Medicinal Chemistry, 2013, Vol. 20, No. 1 11

A simple but successful VS protocol published by Ca-poruscio et al. [165] aimed to discover new aromatase in-hibitors as potential drugs in estrogen receptor positive breast cancer therapy. Cytochrome P450 aromatase (CYP19) catalyzes the rate-limiting step in the biosynthesis of estro-gens by the aromatization of the A ring of androgen precur-sors such as androstenedione and testosterone. Many aro-matase inhibitors are imidazoles or triazoles that bind to the active site of CYP19 crystal structure by coordinating the heme iron atom of the enzyme through a heterocyclic nitro-gen lone pair. The authors carried out a SBVS using the Co-CoCo database of Asinex libraries [45] and GLIDE software [52] in which a metal constraint was set up. Also in this case, the compounds, ordered on the basis of the scores, were prioritized by visual inspection taking into account descrip-tors relevant to receptor-ligand binding such as good pro-tein/ligand complementarity and interactions with residues known to be important from mutagenesis studies or because they interact with known substrates/inhibitors. The screening identified four new core structures with IC50 < 100 nM on the CYP19 aromatase. The most active compound 2 (Chart (1) and (Table 8) showed an IC50 of 9.4nM and favourable ADME properties (predicted log octanol-water partition co-efficient (QPlogPo/w ) = 2.94; predicted log aqueous solubil-ity coefficient (QPlogS) = –2.9; predicted apparent Caco-2 permeability in nm/sec (QPPCaco-2) = 1317; number of primary metabolites = 2).

In another work, Sager et al. [166] identified novel cGMP efflux inhibitors by means of VS. The human ATP binding cassette (ABC) transporter, ABCC5 transports cGMP out of cells, and inhibition of ABCC5 may have cyto-toxic effects. Clinical evidence has shown that extracellular cyclic guanosine monophosphate (cGMP) levels are elevated in various types of cancer. ABCC5 efflux is inhibited by the phosphodiesterase 5 (PDE5) inhibitor sildenafil, and in order to search for potential ABCC5 inhibitors, a refined ABCC5 model was used for VS of sildenafil derivates in a combined approach of the ligand-based and structure based drug de-sign. To retrieve compounds with a common substructure as in sildenafil (a guanine-like moiety resembling the guanine part of cGMP), the Molcart chemical management system, featuring a number of compound databases (including Am-binter, Chembridge, Lifechemicals, etc.) was used. So, 105 sildenafil-like compounds were retrieved and docked using a 4D (multi-conformational) VS docking (ICM software pack-age) [56] into the ABCC5 transporter. 4D VS docking is an original computational technique called “fumigation” capa-ble of generating more “druggable” conformations of ligand binding pockets. Fumigation is based on torsional sampling of the binding pocket side chains in the presence of a repul-sive density representing a generic ligand, using the ICM biased probability Monte Carlo sampling procedure. In this protocol, seven of eleven compounds predicted by VS to bind to ABCC5 were more potent than sildenafil, and the two compounds 3 and 4 showed values of Ki of 75.3 and 65.3 nM, respectively (Chart (1) and Table 8).

Recently, Spadaro et al. [167] used a pharmacophore model to virtually screen a small, in-house library of com-pounds on 17b-hydroxysteroid dehydrogenase type 2 (17 -HSD1) receptor. This latter, which is responsible for the in-tracellular NAD(P)H-dependent conversion of the weak es-

trone E1 into the highly potent estrogen E2, was found over-expressed at mRNA level in breast cancer cells and endome-triosis. An inhibition of this enzyme could selectively reduce the local E2-level thus allowing for a novel, targeted ap-proach in the treatment of estrogen dependent diseases (EDD), like breast cancer, endometriosis and endometrial hyperplasia. A novel pharmacophore model derived from crystallographic data was built by using the Molecular Oper-ating Environment (MOE) [62] and used for the VS of a small in-house library of compounds, thus adopting a LBVS approach. Experimental verification of the virtual hits led to the identification of a moderately active compound that was chemically modified. Rigidification and further structure optimizations resulted in the discovery of a novel class of 17 -HSD1 inhibitors bearing a benzothiazole-scaffold linked to a phenyl ring via keto- or amide-bridge. The most active keto-derivative 5 shows an IC50 value of 44 nM for the trans-formation of E1 to E2 by 17 -HSD1, reasonable selectivity against 17 -HSD2 but pronounced affinity to the estrogen receptors.

In a recent paper, Lin et al. [168] applied an original computational method to predict the promiscuous binding propensities of drug molecules. This study is a successful example of the application of a combined VS approach in the field of polypharmacology, an upcoming branch of pharma-ceutical science dealing with the recognition of the off-target activities of small chemical compounds. In this paper, a multi-task approach of homology modeling, molecular dock-ing and molecular dynamics (MD) simulations was used to predict the potential 5-HT2A off-target activity of FDA ap-proved drugs. G protein-coupled receptors (GPCRs) and kinases are two of the most important drug target families. Many of their ligands are well known to have promiscuous binding propensities within their own protein families. In this study, firstly, an induced-fit protocol was carried out to sample the receptor conformational changes upon binding of ketanserin and cyproheptadine, two representative ligands belonging to class I and class II antagonists of 5-HT2A, re-spectively. The induced fit complexes were used as the start-ing point for further global structure refinement via MD simulation including explicit lipid membrane and water envi-ronment by Desmond software [169]. Then, the refined in-duced fit models were used to virtually screen the ZINC-FDA drug library filtered with drug-like criteria by DOCK software [50] and re-scored by MM-GB/SA method. The first 200 top-ranked solutions were subjected to further struc-tural analysis and visual check. Of the six top scoring hits chosen for experimental assays, surprisingly, one well-known kinase inhibitor, sorafenib, compound 6 (Chart (1) and Table 8) showed unexpected promiscuous binding affini-ties toward 5-HT receptors (Ki = 1959, 56, and 417 nM against 5-HT2A, 5-HT2B, and 5-HT2C, respectively). Soraf-enib was originally developed as a RAF-kinase inhibitor (IC50 = 52 nM) but subsequently it has been shown to be a multikinase inhibitor that also inhibits PDGFR (IC50 = 37 nM), VEGFR2 (IC50 = 59 nM), VEGFR3 (IC50 = 16 nM), c-Kit (IC50 = 31 nM), and FLT1 (IC50 = 31 nM). 5-HT2B is highly expressed in the liver, kidneys, stomach, and gut. Considering that the 5-HT2B binding affinity of sorafenib is in the same therapeutic window as its kinase inhibition ac-tivities, one may hypothesize that the 5-HT2B inhibition

12 Current Medicinal Chemistry, 2013, Vol. 20, No. 1 Lavecchia and Di Giovanni

Table 8. Key Features of Case Studies Reporting Hits with Potency < 1 μM

Cpd. Target Software Assay VS Method Ref.

1 Soluble epoxide

hydrolase (sEH)

FlexX

With CScore

Fluorescence assay SBVS [164]

2 Cytochrome P450 aromatase (CYP19) GLIDE Fluorescence assay SBVS [165]

3, 4 Human ATP binding cassette (ABC)

transporter

ICM cGMP

uptake into inside-out

vesicles (IOV)

Combined LBVS /SBVS [166]

5 Hydroxysteroid Dehydrogenase Type 1

17 -HSD1

MOE 17 -HSD1

cell free assay

Combined pharmacophore

/SB-VS

[167]

6 5-HTHRs MODELLER

DOCK

Desmond

Radiolabeled competitive

binding assay

Retrospective virtual screening [168]

7 Prolyl oligopeptidase (POP/PREP) FITTED Recombinant human

prolyl oligopeptidase

assay on intact living cell

SBVS with scoring function

manually modified

[170]

8 Mitochondrial

enzyme NADH:quinone oxidoreductase

(PfNDH2)

Pipeline Pilot

PowerMV

Spectrophotometric assay LB-VS combined with mo-

lecular fingerprints and

chemoinformatics methods

[172]

9 Microsomal prostaglandin E2 synthase-

1 (mPGES- 1)

Catalyst

Pipeline Pilot

GLIDE Induced Fit

In vitro inhibitory activity

in a cell-free assay

Combined Pharma-

cophore/SB-VS

[174]

10 Cdc25B Phoshatase GLIDE Fluorescence assay SB-VS/substructure search [175]

Table 9. Key Features of Case Studies Reporting Hits with Potency Ranging Between 1-10 μM

Cpd. Target Software Assay VS Method Ref.

11 Acetylcholinesterase (AChE) Discovery Studio Pipeline Pilot

Spectrophotometric assay Pharmacophore VS/ MMGBSA calcula-tions

[176]

12 Sentrin/SUMO-specific protease 1 (SENP-1)

GLIDE In vitro SENP-1 assay SB-VS [178]

13 Aminopeptidase N(APN) FlexX In vitro APN assay SB-VS [179]

14 Cytochrome bc1 of P. falciparum W2 (chloroquine-resistant) strain

GOLD In vitro P. falciparum assay SB-VS [180]

might directly contribute to the anti-cancer effect of soraf-enib. However, the possibility that the activities of sorafenib toward 5-HT receptors might also cause side effects instead of bringing clinical benefits cannot be excluded.

Another study described a rare example of VS applied to the discovery of covalent inhibitors [170]. Prolyl oligopepti-dase (POP/PREP) is a highly conserved and widely distrib-uted postproline endopeptidase. This enzyme, which can accommodate proline and alanine residues in its catalytic site, has been found to cleave neuropeptides in vitro. It has been reported that POP is involved in many functions of the central nervous system and that abnormal POP activity, es-pecially in the brain, is associated with a number of diseases. Literature reports support the formation of a covalent bond

between the catalytic serine of the active site and a reactive functional group such as an aldehyde, hydroxyacetyl or ni-trile present on the inhibitor. So, the authors screened a li-brary of drug-like molecules retrieved from the ZINC collec-tion [40] containing either an aldehyde or a nitrile group with the software FITTED [171]. This program was appropriately modified to automatically identify reactive functional groups for covalent inhibition and form covalent bonds when the catalytic serine and reactive groups were properly posi-tioned. The docked compounds were finally rank-ordered by scores, revealing that nine known inhibitors were ranked on the very top of the list (within the top 1.5%), validating the VS protocol. After visual inspection, a virtual hit molecule together with four analogues was selected for synthesis. The most active compound 7 (Chart (1) and Table 8) exhibited

Virtual Screening Strategies in Drug Discovery: A Critical Review Current Medicinal Chemistry, 2013, Vol. 20, No. 1 13

high nanomolar inhibitory activities with IC50 = 200-890 nM in intact living human cells and acceptable metabolic stabil-ity. The success of the work can be ascribed to an “ad hoc” modification of the scoring function used to screen the com-pounds library, demonstrating that covalent binding and/or geometrical constraints to the ligand/protein complex may lead to an increase in bioactivity.

In a recent paper, Sharma et al. [172] published 48 novel inhibitors of the parasite's mitochondrial enzyme NADH: quinone oxidoreductase (PfNDH2) as new agents against malaria by a modular approach combining chemoinformatic methods and LBVS. In this work, the authors carried out an initial substructure search of the BioFocus library (~750.000 compounds) for compounds which contained the “key moi-ety” of the known inhibitor 1-hydroxy-2-dodecyl-4-(1H)quinolone (HDQ), the [4-(1H)quinolone], obtaining 1175 compounds. A miniaturized spectrophotometric assay revealed the presence of 54 new compounds with IC50 < 20μM against the P. falciparum NDH2 enzyme. The next step of the work was to identify novel chemotypes that ex-hibit the desired biological activity in BioFocus library by combining different VS approaches. Some methods, such as molecular fingerprints, turbo similarity searching and sub-structural searches, aimed to identify bioisosteres of the key moiety of HDQ. Other methods like naiväe Bayesian classi-fier and principle component analysis (PCA) were used for compounds selection. Then, the results were combined to-gether and identified 34356 unique compounds that were predicted by one or more methods to be similar in some way to one or more of the initial set of 54 active compounds. Fi-nally, a best subset of 16000 compounds was achieved using two molecular descriptors like Diversity Selection and Di-versity Assessment implemented in PipeLine Pilot [173]. Among them, 32 diverse compounds were identified to in-hibit PfNDH2 enzyme with IC50 values in nanomolar range. The most active compound 8 exhibited an enzyme inhibition IC50 value of 78 nM (Chart (1) and Table 8), confirming the added value of using multiple ligand-based chemoinformatic approaches in discovering new antimalarials.

A more simple pharmacophore-based VS protocol was applied by Waltenberger et al. [174], aimed to identify chemically diverse inhibitors of microsomal prostaglandin E2 synthase-1 (mPGES- 1), an enzyme catalyzing the pros-taglandin E2 formation and which is considered a potential anti-inflammatory pharmacological target. To discover novel chemical scaffolds active on this enzyme, two pharma-cophore models for acidic mPGES-1 inhibitors were devel-oped and theoretically validated using information on mPGES-1 inhibitors from literature. The models were used to screen chemical databases supplied by the National Can-cer Institute (NCI) and SPECS. Twenty-nine compounds were selected and tested in a cell-free mPEGS-1 assay. Among them, nine chemically diverse compounds showed inhibitory activity on mPGES-1 with IC50 values ranging between 0.4 and 7.9 μM. A representative example of these new mPGES-1 inhibitors (compound 9) is reported in Chart (1). These compounds were also able to inhibit 5-lipoxygenase in cell-free assays and/or in intact cells, sup-porting the multitarget idea.

Another work recently published by Lavecchia et al. [175] described a SBVS approach applied to the Cdc25B dual speci-

ficity phosphatases. Cell division cycle 25 (Cdc25) proteins are highly conserved, dual specificity phosphatases that regu-late cyclin-dependent kinases and represent attractive drug targets for anticancer therapies. To discover more potent and structurally diverse inhibitors of Cdc25, a SBVS strategy was performed by docking 2.1 million compounds using ZINC database and NCI Diversity Set into the Cdc25B active site. The authors carried out a multistep VS using the different modules available in GLIDE software [52]. This program pro-vides a rational workflow for VS by means of three options for speed/accuracy of prediction: a high-throughput virtual screening (HTVS) mode, a standard precision (SP) mode and an extra precision (XP) mode. The HTVS mode can be used as a filtering step to cut down the size of a very large collection of molecules, followed by more accurate docking calculations with the other modes. The application of this "funnel" strategy led to the selection of 23 compounds from the NCI Diversity Set and 7 compounds from the ZINC database and 15 were found to have enzyme inhibition activity at micromolar con-centration. Among them, four structurally diverse compounds with drug-like properties evidenced the most interesting inhi-bition profile with different kinetic characteristics. These com-pounds were found to inhibit human MCF-7, PC-3, and K562 cancer cell proliferation and to significantly affect the cell cycle progression. In particular, three of the four discovered inhibitors showed a behaviour typical of the reversible competitive inhibitors, few examples of which are available in literature. The most active compound 10 (Chart (1) and Table 8) showed an IC50 of 0.24 and 0.1 μM against Cdc25A and Cdc25B respectively and good ADME properties (predicted log octanol-water partition coefficient (QPlogPo/w ) = 0.45; predicted log aqueous solubility coefficient (QPlogS) = –2.7; number of primary metabolites = 5). However, its kinetic be-havior was compatible with that of an irreversible inhibitor of Cdc25 phosphatase isoforms. A subsequent hierarchical simi-larity search with the most active reversible Cdc25B inhibitor found led to the identification of an additional set of 19 ligands, three of which were confirmed as Cdc25B inhibitors with IC50 values of 7.9, 4.2, and 9.9 μM, respectively.

Case Studies Reporting Hits with Potency Ranging Be-tween 1-10 μM

A strategy in which pharmacophore-based virtual screen-ing and MM-GB/SA calculations were combined was carried out by Chen et al. [176] in order to identify new acetylcho-linesterase inhibitors. Acetylcholinesterase (AChE) is one of the most important targets for the treatment of Alzheimer’s disease. A pharmacophore model was generated by Discov-ery Studio platform [177] and it was then employed to virtu-ally screen a compounds library retrieved from ZINC com-mercial database [40] with shape constraints. The hit com-pounds were scored through their molecular binding energies calculated by means of MM/PBSA. Fifteen compounds were selected and purchased in order to test their anti-AChE ef-fects and seven of them showed inhibitory effects with IC50 values ranging from 1.5 to 9.8 μM. The most active com-pound 11 is showed in Chart (2) and (Table 9). It also showed favourable ADME properties (octanol-water distri-bution coefficient logD = 2.01; octanol-water partition coef-ficient logP =2.01).

14 Current Medicinal Chemistry, 2013, Vol. 20, No. 1 Lavecchia and Di Giovanni

In another recent publication, Chen et. al. [178] published a simple VS protocol to identify new inhibitors of Sen-trin/SUMO-specific protease 1, a new target involved in the development of prostatic cancer. Modification of proteins by conjugation of Small Ubiquitin-like Modifier (SUMO) is a key mechanism for regulating many cellular processes. SU-MOylation is a dynamic process and can be reversed by SUMO-specific proteases (SENPs), which are critical in maintaining the balance between the level of SUMOylated and unmodified cellular substrates and hence play an impor-tant role in mediating normal cellular physiology. Recently, SENP1 was found to be over-expressed in over 50% of more than 100 prostatectomy cases with high-grade prostatic intra-epithelial neoplasia (PIN) and prostate cancer. In this study, the authors applied a multistep SBVS protocol similar to those published by Caporuscio et al. [165] and Lavecchia et al. [175], using GLIDE software in SP and XP mode and the SPECS library. In this work, 38 compounds that ranked in the top 100 hits were purchased and biologically tested. Of these, five compounds showed SENP1 inhibition at 50 μM. The most interesting compound 12 showed an IC50 =2.385 μM (Chart (2) and Table 9).

A VS was performed by Feng et al. [179] to discover novel lead structures as potent aminopeptidase N(APN) in-hibitors. The zinc-dependent membrane-bound proteolytic enzyme aminopeptidase N(APN) widely exists in many cells, tissues and organs such as intestinal epithelial, kidney, liver, and lung cells, etc., and could selectively remove amino acids from N-terminal of peptides. Moreover, APN is frequently identified as being over-expressed on tumor cell surfaces, representing a significant target for anti-tumor ther-apy. In this study, SPECS library was filtered by Lipinsky rule and virtually screened by FlexX program [51] against the APN crystal structure active site. 14,829 hits were thereby obtained and 742 compounds were picked out for further analysis of interaction and manual selection. Finally, a total of 24 compounds purchased from the SPECS com-pany were selected for APN inhibitory evaluation. Four compounds with IC50 values lower than 100 μM possessed relatively good inhibitory activity against APN. Compound 13 with IC50 =3.7 μM showed the best performance (Chart (2) and Table 9).

In a combined LBVS and SBVS approach, Rodrigues et al. [180] screened the ZINC and MOE databases to identify

new inhibitors of cytochrome bc1 of multidrug-resistant P. falciparum strains. Compound library was subjected to a SBVS using GOLD software [53] in three consecutive steps and employing different GOLD settings. At first, VS was performed with 7–8 times speed-up settings. This is an opti-mized setting for VS protocols since, in this way, a lower number of genetic operations are done. As a result, a higher throughput is obtained with acceptable accuracy rates in the prediction (60–70%). The best 100 ligands of each database were subjected to further docking refinement, this time with standard settings, that is a higher number of genetic opera-tions, but a relatively low number of runs to allow for a bet-ter prediction of the pose. The final Gold Scores were or-dered and each ligand was visually inspected taking into account the co-crystal ligand and/or hydrogen bonds with key residues of the receptor. 23 compounds were purchased and shifted to in vitro antiplasmodial testing against the P. falciparum W2 (chloroquine-resistant) strain. Out of the 23 compounds tested, 6 showed IC50 in the 2-30 μM range. A representative example of these compounds (compound 14) is reported in Chart (2).

Case Studies Reporting Hits with Potency Ranging Be-tween 10-100 μM

Starting from the crystal structure of the DDC-carbidopa complex, an original VS protocol in three steps, which inte-grates pharmacophore searches and molecular docking, was developed by Daidone et al. [181]. Dopa decarboxylase (DDC), a pyridoxal 5'-phosphate enzyme (PLP), responsible for the biosynthesis of dopamine and serotonin, is involved in Parkinson’s disease. A pharmacophore model was gener-ated and validated using an in-house built database of known active and inactive DDC inhibitors. This model was first used to filter the lead-like and the drug-like subsets of the public ZINC database [40] which are tailored to an extended Lipinski’s rule of five. Compounds satisfying the pharma-cophoric requirements were then instrumental to running docking studies. Compounds showing the highest binding scores were selected, and tested in vitro for their ability to bind and inhibit purified recombinant human DDC. The compounds with the highest inhibitory activity were used to perform a second similarity-based filtering of the public ZINC database to retrieve analogs in order to expand the new classes of DDC inhibitors. The in vitro testing revealed

NH

N

HN

O

S

O

N

14

IC50 = 2.0 M

11

IC50 = 1.5 M

N

N

OO

NHS

O O

NH

O

HN

O

O

O

Cl

O

12

IC50 = 2.4 M

N

O

O

O

Br

O

HNOH

13

IC50 = 3.7 M

Chart (2). Chemical structures of hits with potency ranging between 1-10 μM.

Virtual Screening Strategies in Drug Discovery: A Critical Review Current Medicinal Chemistry, 2013, Vol. 20, No. 1 15

that 9 hits sorted out from the second screening inhibit hu-man DDC in a competitive mode with Ki values in the range 2–15 μM. The most interesting compound 15 showed an IC50 = 12.8 μM (Chart (3) and Table 10). By performing an in silico similarity search on the core structure of 16, followed by a substructure search, several competitive inhibitors of human DDC with Ki values in the low micromolar range were identified. However, they were unable to bind free PLP, and were predicted not to cross the blood-brain barrier.

Cao et al. [182], aiming to discover novel tubulin inhibi-tors, performed SBVS against the colchicine-binding pocket. Microtubules are critical for the function of eukaryotic cells, including the formation of cytoskeleton and spindle, which play essential roles in cellular architecture maintenance and chromosome segregation. Because of their crucial role in mitotic events, microtubules serve as important drug targets for anticancer chemotherapy. The authors applied a hierar-chical strategy that consisted of three steps: (1) Prediction of the ligand binding poses using a fast compute docking pro-gram; (2) optimization and rescoring of the docked ligands in protein binding pocket using molecular mechanics force field in combination with generalized born surface area (GB/SA) implicit solvent; and (3) MD simulations of the top scoring complex in explicit water in which the ligand binding free energy was estimated by ensemble averaging the gas-phase enthalpy and the molecular mechanics Poisson Boltzmann surface area (MM-PB/SA) method. In addition, predefined structural filters were applied to eliminate the undesired hits after each step of ranking and scoring, and the hydrogen-bond interaction was defined using more stringent criteria in analyzing MM-GB/SA and MM-PB/SA results (i.e., distance threshold of 3.5 Å). This strategy automatically resulted in 63 candidates meeting the structural and energetic criteria from an in-house screening library constructed by ChemDiv compound database containing approximately 100,000 di-

verse drug-like compounds. Among them, nine molecules were chosen for experimental validation, all of which shared the similar binding pose and contained an enriched scaffold bearing thiophene core. Encouragingly, five compounds were active in the tubulin polymerization assay. The most interesting compound 16 showed an IC50 value of 14.2 μM (Chart (3) and Table 10).

A docking virtual screening targeting the urokinase re-ceptor (uPAR) was carried out recently by Wang et al. [183], which produced the first “small molecules” with inhibitory activity against uPA-uPAR complex. uPAR is a cell surface glycosylphosphatidylinositol (GPI)-anchored protein that has been widely implicated in promoting the metastasis of many types of cancers. The receptor enhances pericellular prote-olysis by serving as a docking site to the urokinase-type plasminogen activator (uPA), triggering a cascade of prote-olytic events that include activation of plasminogen and ma-trix metalloproteinases (MMPs). uPAR is able to regulate cell adhesion, migration, proliferation and survival, inde-pendently of the uPA proteolytic activity. Despite the lack of a transmembrane domain, uPAR activates intracellular sig-naling by interacting with other transmembrane receptors. So 300,000 compounds of the ChemDiv library were docked in UPA binding pocket by AUTODOCK4 program [49]. The resulting complexes were scored by different scoring func-tions such as ChemScore, GOLD, AUTODOCK, and DOCK. The top ranking 200 compounds for each scoring function were visualized, and clustered by chemical similar-ity. About 50 compounds were selected among the top hits from each scoring function. A total of 210 compounds were acquired from ChemDiv and biochemically evaluated. The most active compounds 17 and 18 displayed a Ki of 18 and 30 μM, respectively, through a fluorescence polarization assay against uPA-uPAR complex (Chart (3) and Table 10). Interestingly, two analogues of 17 were shown to block an-

OH

OH

O

O

15

IC50 = 12.8 M

S

HN

NH2O

O

S

16

IC50 = 14 M

O

ONH

O

O

N

N

O

NH

HN

17

Ki =18 M

18

Ki =30 M

SO2

N

NNO

HN

F

CH3

NHSO2CH3

20

IC50 = 21 M

O

S

NN

N

N

O O

Cl

21

IC50 = 10 M

O

O

N

OO

O

HO

22

IC50 = 45 M

N

OO

O

HO

23

IC50 = 20 M

O

O

SO2

N

NNO

F

CH3

19

OCH3

Chart (3). Chemical structures of hits with potency ranging between 10-100 μM.

16 Current Medicinal Chemistry, 2013, Vol. 20, No. 1 Lavecchia and Di Giovanni

giogenesis, to inhibit cell growth and to induce apoptosis, revealing some encouraging effects on metastatic progres-sion by in vivo studies.

A recent SBVS study [184] followed by rational drug de-sign, organic synthesis, and biological testing led to the iden-tification of novel inhibitors of the NS5B RNA-dependent RNA polymerase. NS5B is a RNA-dependent-RNA polym-erase which catalyzes the synthesis of progeny viral RNA strands and, because of its apparent sequence and structural difference with human DNA and RNA polymerases, repre-sents an attractive target for the development of selective inhibitors. To date, a variety of nucleoside and non-nucleoside Hepatitis C virus (HCV) polymerase inhibitors have been reported. The non nucleoside inhibitors (NNIs) can be divided into five groups based on the distinct allos-teric binding site on the HCV polymerase they target, namely thumb site I (TSI), thumb site II, (TSII), palm site I (PSI), palm site II (PSII), and palm site III (PSIII). The authors applied a simple SBVS protocol on the NS5B allos-teric binding site PSI by using an “in-house” library of 2692 molecules synthesized and/or published by their research group. Docking was performed by means of the GLIDE VS workflow [52]. The top-ranked compounds were visually inspected, searching for virtual hits endowed with high dock-ing score and also able to create the key interactions with the PSI residues. Compound 19 (Chart (3) and Table 10), having a pyrazolobenzothiazine scaffold, was predicted to bind in a fashion somewhat similar to that of known PSI-NNI inhibi-tors establishing some of the key ligand-NS5B interactions. So, a computer-driven optimization of 19 was carried out in order to design and synthesize a series of new pyrazoloben-zothiazine-based compounds. Among them, the best com-pound was a meta-fluoro-N-1-phenyl pyrazolobenzothiazine derivative, compound 20, which exhibited an IC50 of 21.0 μM by a NSC5B functional assay and an EC50 = 3.6 μM,

EC90 = 25.6 μM, and CC50 > 180 μM in the human hepatoma (Huh 9 13) replicon system.

Another interesting paper published by Jansen et al. [185] in 2013 described the discovery of novel Trypanosoma brucei phosphodiesterase B1 (TbrPDEB1) inhibitors. TbrPDEB1, an enzyme which selectively catalyzes the hy-drolysis of cAMP to AMP, has recently been validated as new therapeutic target for human African trypanosomiasis. In this study, the authors performed a SBVS study using the new TbrPDEB1 crystal structure and a customized VS method that combines a docking scoring function (PLANTS) [186] with a protein ligand interaction fingerprint (IFP) [110] scoring method to rank molecular docking poses of 385,000 commercially molecules available from ZINC data-base [40]. By this combined method, 29 compounds were selected and purchased. Among them, 6 were found to in-hibit TbrPDEB1 with IC50 values below 100 μM. The best compound 21 showed an IC50 value of 10 μM (Chart (3) and Table 10).

Finally, a recent original approach was applied by Lavec-chia et al. [187] in discovering novel small molecule inhibi-tors targeting the frataxin/ubiquitin interaction for the treat-ment of Friedreich ataxia (FRDA). FRDA is an autosomal recessive neuro- and cardiodegenerative disorder for which there are no proven effective treatments. FRDA is caused by decreased expression and/or function of the mitochondrial protein frataxin. In this study, the authors first reported find-ings that frataxin is degraded via the ubiquitin–proteasomal pathway and that it is ubiquitinated at residue K147 in Calu-6 cells. Then, a theoretical model of the frataxin-K147/ubiquitin complex was constructed considering the formation of a co-valent isopeptide bond between the carboxyl group of the C-terminal G76 of ubiquitin and the -NH2 group of frataxin K147 with the help of the HADDOCK algorithm [188]. The residues interacting across the frataxin-K147/ubiquitin com-plex interface were predicted by two interface prediction

Table 10. Key Features of Case Studies Reporting Hits with Potency Ranging Between 10-100 μM

Cpd. Target Software Assay VS Method. Ref.

15 Dopa decarboxylase

(DDC)

Dovis

AUTODOCK Vina Spectrophotometric assay

Combined pharmacophore/SB-VS / sub-

structure search [181]

16 Tubulin

Dock

Amber

PLOP

Turbidimetric assay Combined SB-VS/molecular dynamics

/MMGBSA/substructure search [182]

17, 18 uPAR AUTODOCK4 Fluorescence polarization

assay SB-VS [183]

19, 20 NS5B RNA-dependent

RNA polymerase GLIDE

Liquid scintillation counter

of RNA products SB-VS [184]

21 Trypanosoma brucei

phosphodiesterase B1

PLANTS

Protein ligand interaction

fingerprint (IFP) Biogen

Idec

Scintillation proximity assay SB-VS

combined with molecular fingerprints [185]

22, 23 Frataxin GLIDE

AUTODOCK4 Densitometry scanner analysis Combined SB-VS /substructure search [187]

Virtual Screening Strategies in Drug Discovery: A Critical Review Current Medicinal Chemistry, 2013, Vol. 20, No. 1 17

programs, WHISCY [189] and ProMate [190]. Afterwards, the authors went on to search for small molecules capable of directly targeting the frataxin region that binds ubiquitin in order to prevent frataxin/ubiquitin association. So, a multi-step SBVS of more than 65,000 lead-like compounds ob-tained from the NCI Open Database was carried out using two docking programs, GLIDE [52] and AUTODOCK [49]. 13 consensus hits were selected and tested experimentally for their ability to prevent the frataxin ubiquitination. The most interesting molecule, compound (±)-22, was found to be the most effective in preventing the frataxin ubiquitina-tion. Since compound 22 was tested as a racemic mixture, it was synthesized and resolved in its enantiomers (+)-22 and (-)-22, which were assayed individually and compared to the racemate. In comparison with the racemate, (+)-22 indirectly induced a larger increase in the cellular concentration of ma-ture frataxin by significantly restoring the endogenous level of frataxin precursor. In contrast, (-)-22 displayed no in-crease in the cellular concentration of frataxin, indicating a fine degree of selectivity in the binding site due to chiral geometry. The IC50 of 22 for inhibition of frataxin ubiquiti-nation was determined to be 45 μM (Chart (3) and Table 10). Additionally, compound 22 revealed interesting ADME properties (predicted log octanol-water partition coefficient (QPlogPo/w ) = 2.4; predicted log aqueous solubility coeffi-cient (QPlogS) = –1.5; predicted apparent Caco-2 permeabil-ity in nm/sec (QPPCaco-2) = 1322; number of primary me-tabolites = 7). Substructure and similarity follow-up searches on the most active hit compound 22 yielded a series of mor-pholino analogues with a key meta- and para-methoxy sub-stituted phenyl ring that possessed activity in the micromolar range. The most active compound 23 showed an IC50 of 20 μM (Chart (3) and Table 10).

CONCLUDING REMARKS

Herein we have provided a brief overview of the current state-of-the-art in both LBVS and SBVS approaches as well as in computational chemogenomics techniques, emphasiz-ing the great progress and changes that in silico VS has made over the past years, making it a valuable and efficient tool for drug discovery. Despite the numerous successful studies and the very positive picture that is often drawn, the effec-tiveness and impact of SBVS and LBVS are currently lim-ited by major scientific problems that are far from being solved. Firstly, the problems include imprecise pose scoring and binding energy predictions as well as incorrect similar-ity-based compound rankings all of which require time-consuming follow-up analysis to select candidate leads on the basis of knowledge or intuition. In addition, although ligands are commonly handled with full flexibility, the pro-tein flexibility is still, at best, only partially considered. Fur-ther studies are still necessary to tackle this issue and address the induced-fit problem. Also, the dynamic inclusion of wa-ter molecules during the docking process, to take account of eventually important water-mediated hydrogen bond bridges between the ligand and the protein, could increase the effi-ciency of the approach. On the other hand, LBVS methods, ranging from rather simplistic fingerprint-based approaches to highly complex machine learning techniques, still appear approximate and much depend on the type of molecular rep-resentations that are utilized. Furthermore, all methods dis-

play a yet unpredictable compound class dependency that is again much influenced by chosen molecular representations. Hence, practical VS protocols should best employ multiple methods of different design. Care must be taken to design compound selection strategies that take into account the po-tential strengths and weaknesses of different approaches and do not entirely rely on consensus approaches. Overall, SBVS applications currently dominate the VS field. However, the majority of docking hits (but not all) are only weakly potent. LBVS methods, albeit less frequently applied, display a ten-dency to detect more potent hits, particularly if combinations of 2D and 3D similarity-based methods are used. Further-more, although the availability of targets for which both ligand and 3D structure information is progressively increas-ing, integrated SBVS and LBVS approaches continue to be infrequent. Nevertheless, despite their limitations, both LBVS and SBVS approaches have produced considerable success stories, leading in some cases to molecules with fa-vorable preclinical outcomes [191-199]. Moreover, poly-pharmacology represents a promising growth area for VS. Computational techniques and data models that enable the proteome-wide study of protein-ligand interactions and the correlations of molecular interactions with clinical outcomes will provide us with valuable clues as to the molecular basis of cellular function, thereby facilitating a shift in the conven-tional one-drug-one-target drug discovery process to a new paradigm of polypharmacology. A number of studies have proven that such a methodology is particularly useful for both understanding the molecular mechanisms of drug side effects, and re-purposing safe pharmaceuticals to target dif-ferent pathways and/or treat different diseases. In spite of tremendous advances in chemoinformatics and bioinformat-ics, predicting protein-ligand interactions on a proteome-wide scale is a long way from being realized. The current computational chemogenomics techniques have their own limitations in terms of both their predictive power and target coverage. In order to progressively predict polypharma-cological effects, it should integrate ligand- and phenotype-based approaches with target-based methodologies. Thus, we can conclude that VS methods play a considerable role in drug-discovery research although there are clearly opportuni-ties to further increase the impact of computational screening technologies.

CONFLICT OF INTEREST

The authors confirm that this article content has no con-flicts of interest.

ACKNOWLEDGEMENTS

This work was accomplished thanks to the financial sup-port of the Ministero dell’Istruzione, dell’Università e della Ricerca (MIUR-PRIN 2010-2011, prot. 2010W7YRLZ_003) and the Cariplo Foundation (file 2009-2727).

REFERENCES

[1] Song, C. M.; Lim, S. J.; Tong, J. C. Recent advances in computer-aided drug design. Brief. Bioinform., 2009, 10(5), 579-591.

[2] Dror, O.; Shulman-Peleg, A.; Nussinov, R.; Wolfson, H. J. Predict-ing molecular interactions in silico: i. a guide to pharmacophore identification and its applications to drug design. Curr. Med.

Chem., 2004, 11(1), 71-90.

18 Current Medicinal Chemistry, 2013, Vol. 20, No. 1 Lavecchia and Di Giovanni

[3] Jahn, A.; Hinselmann, G.; Fechner, N.; Zell, A. Optimal assign-ment methods for ligand-based virtual screening. J. Cheminform., 2009, 1, 14.

[4] Villoutreix, B. O.; Renault, N.; Lagorce, D.; Sperandio, O.; Mon-tes, M.; Miteva, M. A. Free resources to assist structure-based vir-tual ligand screening experiments. Curr. Protein Pept. Sci., 2007, 8(4), 381-411.

[5] Dobson, C.M. Chemical space and biology. Nature, 2004, 432(7019), 824-828.

[6] Bohacek, R. S.; McMartin C.; Guida, W. C. The art and practice of structure-based drug design: a molecular modeling perspective. Med. Res. Rev., 1996, 16(1), 3-50.

[7] Ertl P. Cheminformatics analysis of organic substituents: identifica-tion of the most common substituents, calculation of substituent properties, and automatic identification of drug-like bioisosteric groups. J. Chem. Inf. Comput. Sci., 2003, 43(2), 374-380.

[8] Selzer, P.; Roth, H.-J.; Ertl, P.; Schuffenhauer, A. Complex mole-cules: do they add value? Curr. Opin. Chem. Biol., 2005, 9(3), 310-316.

[9] Newman, D.J.; Cragg, G.M.; Snader, K.M. Natural products as sources of new drugs over the period 1981-2002. J. Nat. Prod., 2003, 66(7), 1022-1037.

[10] Feher, M.; Schmidt, J.M. Property distributions: differences be-tween drugs, natural products, and molecules from combinatorial chemistry. J. Chem. Inf. Comput. Sci., 2003, 43(1), 218-227.

[11] Hann, M. M.; Oprea, T. I. Pursuing the leadlikeness concept in pharmaceutical research. Curr. Opin. Chem. Biol., 2004, 8(3), 255-263.

[12] Lipinski, C.A. Drug-like properties and the causes of poor solubil-ity and poor permeability. J. Pharmacol. Toxicol. Methods, 2000, 44(1), 235-249.

[13] Ridder, L.; Wang, H.; de Vlieg, J.; Wagener, M. Revisiting the rule of five on the basis of pharmacokinetic data from rat. ChemMed-

Chem, 2011, 6(11), 1967-1970. [14] Congreve M.; Carr R.; Murray C.; Jhoti H. A 'rule of three' for

fragment-based lead discovery? Drug Discov. Today, 2003, 8(19), 876-877.

[15] Hughes, J.D.; Blagg, J.; Price, D.A.; Bailey, S.; Decrescenzo, G.A.; Devraj, R.V.; Ellsworth, E.; Fobian, Y.M.; Gibbs, M.E.; Gilles, R.W.; et al. Physiochemical drug properties associated with in vivo toxicological outcomes. Bioorg. Med. Chem. Lett. 2008, 18(17), 4872-4875.

[16] Thorne, N.; Auld, D. S.; Inglese, J. Apparent activity in high-throughput screening: origins of compound-dependent assay inter-ference. Curr. Opin. Chem. Biol., 2010, 14(3), 315-324.

[17] Axerio-Cilies, P.; Castaneda, I.P.; Mirza, A.; Reynisson, J. Investi-gation of the incidence of ‘‘undesirable’’ molecular moieties for high throughput screening compound libraries in marketed drug compounds. Eur. J. Med. Chem., 2009, 44(3), 1128-1134.

[18] Benigni, R.; Bossa, C. Mechanisms of chemical carcinogenicity and mutagenicity: a review with implications for predictive toxi-cology. Chem. Rev., 2011, 111(4), 2507-2536.

[19] Enoch, S.J.; Ellison, C.M.; Schultz, T.W.; Cronin, M.T. A review of the electrophilic reaction chemistry involved in covalent protein binding relevant to toxicity. Crit. Rev. Toxicol. 2011, 41(9), 783-802.

[20] Erve, J.C. Chemical toxicology: reactive intermediates and their role in pharmacology and toxicology. Expert Opin. Drug Metab. Toxicol., 2006, 2(6), 923-946.

[21] Kalgutkar, A.S.; Gardner, I.; Obach, R.S.; Shaffer, C.L.; Callegari, E.; Henne, K.R.; Mutlib, A.E.; Dalvie, D.K.; Lee, J.S.; Nakai, Y.; et al. A comprehensive listing of bioactivation pathways of organic functional groups. Curr. Drug Metab., 2005, 6(3), 161-225.

[22] Kazius, J.; McGuire, R.; Bursi, R. Derivation and validation of toxicophores for mutagenicity prediction. J. Med. Chem., 2005, 48(1), 312-320.

[23] Rishton, G.M. Nonleadlikeness and leadlikeness in biochemical screening. Drug Discov. Today, 2003, 8(2), 86-96.

[24] Seidler, J.; McGovern, S. L.; Doman, T. N.; Shoichet, B. K. Identi-fication and prediction of promiscuous aggregating inhibitors among known drugs. J. Med. Chem., 2003, 46(21), 4477-4486.

[25] Feng, B.Y.; Simeonov, A.; Jadhav, A.; Babaoglu, K.; Inglese, J.; Shoichet, B.K.; Austin, C.P. A high-throughput screen for aggrega-tion-based inhibition in a large compound library. J. Med. Chem., 2007, 50(10), 2385-2390.

[26] Baell, J.B.; Holloway, G.A. New substructure filters for removal of pan assay interference compounds (PAINS) from screening librar-ies and for their exclusion in bioassays. J. Med. Chem., 2010, 53(7), 2719-2740.

[27] Rishton, G.M. Nonleadlikeness and leadlikeness in biochemical screening. Drug Discov. Today, 2003, 8(2), 86-96.

[28] Rishton, G.M. Reactive compounds and in vitro false positives in HTS. Drug Discov. Today, 1997, 2(9), 382-384.

[29] Roche, O.; Schneider, P.; Zuegge, J.; Guba, W.; Kansy, M.; Alanine, A.; Bleicher, K.; Danel, F.; Gutknecht, E.M.; Rogers-Evans, M.; Neidhart, W.; Stalder, H.; Dillon, M.; Sjogren, E.; Fotouhi, N.; Gillespie, P.; Goodnow, R.; Harris, W.; Jones, P.; Ta-niguchi, M.; Tsujii, S.; von der Saal, W.; Zimmermann, G.; Schneider, G. Development of a virtual screening method for iden-tification of "frequent hitters" in compound libraries. J. Med.

Chem., 2002, 45(1), 137-142. [30] Crisman, T. J.; Parker, C. N.; Jenkins, J. L.; Scheiber, J.; Thoma,

M.; Bin Kang, Z.; Kim, R.; Bender, A.; Nettles, J. H.; Davies, J. W.; Glick, M. Understanding false positives in reporter gene as-says: in silico chemogenomics approaches to prioritize cell-based HTS data. J. Chem. Inf. Model., 2007, 47(4), 1319 1327.

[31] Huth, J.R.; Mendoza, R.; Olejniczak, E.T.; Johnson, R.W.; Cothron, D.A.; Liu, Y.; Lerner, C.G.; Chen, J.; Hajduk, P.J. ALARM NMR: a rapid and robust experimental method to detect reactive false positives in biochemical screens. J. Am. Chem. Soc., 2005, 127(1), 217-224.

[32] Baell, J. B. Observations on screening-based research and some concerning trends in the literature. Future Med. Chem., 2010, 2(10), 1529-1546.

[33] Lagorce, D.; Maupetit, J.; Baell, J.; Sperandio, O.; Tufféry, P.; Miteva, M.A.; Galons, H.; Villoutreix, B.O. The FAF-Drugs2 server: a multistep engine to prepare electronic chemical compound collections. Bioinformatics, 2011, 27(14), 2018-2020.

[34] Bender, A.; Glen, R. C. Molecular similarity: a key technique in molecular informatics. Org. Biomol. Chem., 2004, 2(22), 3204-3218.

[35] Krier M.; Bret G.; Rognan D. Assessing the scaffold diversity of screening libraries. J. Chem. Inf. Model., 2006, 46(2), 512-524.

[36] Ekins, S.; Williams, A.J. Precompetitive preclinical ADME/Tox data: set it free on the web to facilitate computational model build-ing and assist drug development. Lab. Chip., 2010, 10(1), 13-22.

[37] Ertl, P.; Jelfs, S. Designing drugs on the internet? Free web tools and services supporting medicinal chemistry. Curr. Top. Med.

Chem., 2007, 7(15), 1491-1501. [38] Gozalbes, R.; Pineda-Lucena, A. Small molecule databases and

chemical descriptors useful in chemoinformatics: an overview. Comb. Chem. High Throughput Screen., 2011, 14(6), 548-558.

[39] Richard, A.M.; Gold, L.S.; Nicklaus, M.C. Chemical structure indexing of toxicity data on the internet: moving toward a flat world. Curr. Opin. Drug Discov. Devel., 2006, 9(3), 314-325.

[40] Irwin, J.J.; Shoichet, B.K. ZINC: a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model., 2005, 45(1), 177-182.

[41] Chen, J.H.; Linstead, E.; Swamidass, S.J.; Wang, D.; Baldi, P. ChemDB update: full-text search and virtual chemical space. Bioin-

formatics, 2007, 23(17), 2348-2351. [42] Seiler, K.P.; George, G.A.; Happ, M.P.; Bodycombe, N.E.; Carrin-

ski, H.A.; Norton, S.; Brudz, S.; Sullivan, J.P.; Muhlich, J.; Ser-rano, M.; Ferraiolo, P.; Tolliday, N.J.; Schreiber, S.L.; Clemons, P.A. ChemBank: a small-molecule screening and cheminformatics resource database. Nucleic Acids Res., 2008, 36(database issue), D351-D359.

[43] Wishart, D.S.; Knox, C.; Guo, A.C.; Shrivastava, S.; Hassanali, M.; Stothard, P.; Chang, Z.; Woolsey, J. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids

Res., 2006, 34(database issue), D668-D672. [44] Knox, C.; Law, V.; Jewison, T.; Liu, P.; Ly, S.; Frolkis, A.; Pon,

A.; Banco, K.; Mak, C.; Neveu, V.; Djoumbou, Y.; Eisner, R.; Guo, A. C.; Wishart, D. S. DrugBank 3.0: a comprehensive re-source for “omics” research on drugs. Nucleic Acids Res., 2011, 39(database issue), D1035-D1041.

[45] Gaulton, A.; Bellis, L. J.; Bento, A. P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; Overington, J. P. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res., 2012, 40(D1), D1100-D1107.

Virtual Screening Strategies in Drug Discovery: A Critical Review Current Medicinal Chemistry, 2013, Vol. 20, No. 1 19

[46] Olah, M.; Mracec, M.; Ostopovici, L.; Rad, R.; Bora, A.; Hada-ruga, N.; Olah, I.; Banda, M.; Simon, Z.; Mracec, M.; and Oprea, T.I. In: Chemoinformatics in Drug Discovery; Oprea, Ed.; Wiley-VCH Verlag GmbH.: Weinheim, 2005; Vol. 23, pp. 221-239.

[47] Del Rio, A.; Barbosa, A. J. M.; Caporuscio, F.; Mangiatordi, G. F. CoCoCo: a free suite of multiconformational chemical databases for high-throughput virtual screening purposes. Mol. BioSyst., 2010, 6(11), 2122-2128.

[48] Bissantz, C.; Folkers, G.; Rognan, D. Protein-based virtual screen-ing of chemical databases. Evaluation of different docking/scoring combinations J. Med. Chem., 2000, 43(25), 4759-4767.

[49] Morris, G. M.; Goodsell, D. S.; Halliday, R.S.; Huey, R.; Hart, W. E.; Belew, R. K.; Olson, A. J. Automated docking using a La-marckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem., 1998, 19(14), 1639-1662.

[50] Ewing, T. J.; Makino, S.; Skillman, A. G.; Kuntz, I. D. DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J. Comput. Aided Mol. Des. 2001, 15(5), 411-428.

[51] Warren, G. L. et al. A critical assessment of docking programs and scoring functions. J. Med. Chem., 2006, 49(20), 5912-5931.

[52] Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shaw, D. E.; Shelley, M.; Perry, J. K.; Francis, P.; Shenkin, P. S. Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem., 2004, 47(7), 1739-1749.

[53] Verdonk, M. L.; Cole, J. C.; Hartshorn, M. J.; Murray C. W.; Tay-lor, R. D. Improved protein-ligand docking using GOLD Proteins, 2003, 52(4), 609-623.

[54] Grosdidier, A.; Zoete, V.; Michielin, O. EADock: Docking of small molecules into protein active sites with a multiobjective evolution-ary optimization. Proteins, 2007, 67(4), 1010-1025.

[55] Jain, A. N. Surflex-Dock 2.1: Robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search. J. Comput. Aided Mol. Des. 2007, 21(5), 281-306.

[56] Abagyan, R.; Totrov, M.; Kuznetsov, D. N. ICM: a new method for protein modeling and design. Applications to docking and structure prediction from the distorted native comformation. J. Comput.

Chem., 1994, 15(5), 488 506. [57] Böhm, H. J. The development of a simple empirical scoring func-

tion to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J. Comput. Aided Mol. De-

sign, 1994, 8(3), 243-256. [58] Ravitz, O.; Zsoldos, Z.; Simon, A. Improving molecular docking

through eHiTS' tunable scoring function. J. Comput. Aided Mol. Des., 2011, 25(11), 1033-1051.

[59] Zavodszky, M. I.; Rohatgi, A.; Van Voorst, J. R.; Yan, H.; Kuhn, L. A. Scoring ligand similarity in structure-based virtual screening. J. Mol. Recognit., 2009, 22(4), 280-292.

[60] Gray, J. J.; Moughan, S. E.; Wang, C.; Schueler-Furman, O.; Kuhlman, B.; Rohl, C. A.; Baker, D. Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. J. Mol. Biol., 2003, 331(1), 281-299.

[61] Thomsen, R.; Christensen, M. H. MolDock: A new technique for high-accuracy molecular docking. J. Med. Chem., 2006, 49(11), 3315-3321.

[62] Molecular Operating Environment (MOE), 2012.10; Chemical Computing Group Inc., 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2012.

[63] McGann, M. FRED and HYBRID docking performance on stan-dardized datasets. J. Comput. Aided Mol. Des., 2012, 26(8), 897-906.

[64] Chen, R.; Li, L.; Weng, Z. ZDOCK: An initial-stage protein-docking algorithm. Proteins, 2003, 52(1), 80-87.

[65] Kitchen, D. B.; Decornez, H.; Furr, J. R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: methods and appli-cations. Nat. Rev. Drug Discov., 2004, 3(11), 935-949.

[66] Brooijmans, N.; Kuntz, I. D. Molecular recognition and docking algorithms. Annu. Rev. Biophys. Biomol. Struct., 2003, 32, 335-373.

[67] Moitessier, N.; Englebienne, P.; Lee, D.; Lawandi, J.; Corbeil, C. R. Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. Br. J. Pharmacol., 2008, 153(S1), S7-S26.

[68] Huang, S-Y.; Grinter, S. Z.; Zou, X. Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions. Phys. Chem. Chem. Phys., 2010, 12(40), 12899-12908.

[69] Krovat, E. M.; Steindl, T.; Langer, T. Recent advances in docking and scoring. Curr. Comput. Aided Drug Des., 2005, 1(1), 93-102.

[70] Jain, A. N. Scoring functions for protein-ligand docking. Curr.

Protein Pept. Sci., 2006, 7(5), 407-420. [71] Meng, E. C.; Shoichet B. K.; Kuntz, I. D. Automated docking with

grid-based energy evaluation. J. Comput. Chem., 1992, 13(4), 505-524.

[72] Goodsell, D. S.; Olson, A. J. Automated docking of substrates to proteins by simulated annealing. Proteins: Str. Func. Genet., 1990, 8(3), 195-202.

[73] Shoichet, B. K.; Leach, A. R.; Kuntz, I. D. Ligand solvatation in molecular docking. Proteins: Struct. Funct. Genet., 1999, 34(1), 4-16.

[74] Zou, X.; Sun, Y.; Kuntz, I. D. Inclusion of solvation in ligand bind-ing free energy calculations using the generalized-born model. J.

Am. Chem. Soc., 1999, 121(35), 8033-8043. [75] Wang, J.; Morin, P.; Wang, W.; Kollman, P. A. Use of MM-PBSA

in reproducing the binding free energies to HIV-1 RT of TIBO de-rivatives and predicting the binding mode to HIV-1 RT of efavirenz by docking and MM-PBSA. J. Am. Chem. Soc., 2001, 123(22), 5221-5230.

[76] Böhm, H. J. The development of a simple empirical scoring func-tion to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J. Comput. Aided Mol. Des., 1994, 8(3), 243-256.

[77] Weng, Z.; Vajda, S.; Delisi, C. Prediction of protein complexes using empirical free energy functions. Protein Science, 1996, 5(4), 614-626.

[78] Schulz-Gasch, T.; Stahl, M. Scoring functions for protein-ligand interactions: a critical perspective. Drug Discovery Today Technol., 2004, 1(3), 231-239.

[79] Kulharia, M.; Goody, R.S.; Jackson, R.M. Information theory-based scoring function for the structure-based prediction of protein-ligand binding affinity. J. Chem. Inf. Model., 2008, 48(10), 1990-1998.

[80] Huang, S-Y; Zou, X. Inclusion of solvation and entropy in the knowledge-based scoring function for protein-ligand interactions. J. Chem. Inf. Model., 2010, 50(2), 262-273.

[81] Leach, A. R.; Shoichet, B. K.; Peishoff, C. E. Prediction of pro-tein ligand interactions. Docking and scoring: successes and gaps. J. Med. Chem., 2006, 49(20), 5851- 5855.

[82] O’Boyle, N. M.; Liebeschuetz, J. W.; Cole, J. C. Testing assump-tions and hypotheses for rescoring success in protein ligand dock-ing. J. Chem. Inf. Model., 2009, 49(8), 1871- 1878.

[83] Kitchen, D. B.; Decornez, H.; Furr, J. R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: methods and appli-cations. Nat. Rev. Drug Discovery, 2004, 3(11), 935- 949.

[84] Raub, S.; Steffen, A.; Kämper, A; Marian, C. M. AIScore: chemi-cally diverse empirical scoring function employing quantum chemical binding energies of hydrogen-bonded complexes. J. Chem. Inf. Model., 2008, 48(7), 1492-1510.

[85] Seifert, M.H. Robust optimization of scoring functions for a target class. J. Comput. Aided Mol. Des., 2009, 23(9), 633-644.

[86] Charifson, P. S.; Corkery, J. J.; Murcko, M. A.; Walters, W. P. Consensus scoring: a method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J. Med. Chem., 1999, 42(25), 5100-5109.

[87] Feher, M. Consensus scoring for protein-ligand interactions. Drug Discov. Today, 2006, 11(9-10), 421-428.

[88] Evers, A.; Hessler, G.; Matter, H.; Klabunde, T. Virtual screening of biogenic amine-binding G-protein coupled receptors: comparative evaluation of protein- and ligand-based virtual screening protocols. J. Med. Chem., 2005, 48(17), 5448-5465.

[89] Jiang, F.; Kim, S. H. ‘‘soft docking’’: matching of molecular sur-face cubes. J. Mol. Biol., 1991, 219(1), 79-102.

[90] Perez, A.; Yang, Z.; Bahar, I.; Dill, K. A.; MacCallum, J. L. FlexE: using elastic network models to compare models of protein struc-ture. J. Chem. Theory Comput., 2012, 8(10), 3985-399.

[91] Sherman, W.; Day, T.; Jacobson, M. P.; Friesner, R. A.; Farid, R. Novel procedure for modeling ligand/receptor induced fit effects. J. Med. Chem., 2006, 49(2), 534-553.

20 Current Medicinal Chemistry, 2013, Vol. 20, No. 1 Lavecchia and Di Giovanni

[92] Lin, J. H.; Perryman, A. L.; Schames, J. R.; McCammon, J. A. The relaxed complex method: Accommodating receptor flexibility for drug design with an improved scoring scheme. Biopolymers, 2003, 68(1), 47-62.

[93] Amaro, R. E.; Baron, R.; McCammon, J. A. An improved relaxed complex scheme for receptor flexibility in computer-aided drug de-sign. J. Comput. Aided Mol. Des., 2008, 22(9), 693-705.

[94] Case, D. A.; Cheatham, T. E. 3rd; Darden, T.; Gohlke, H.; Luo, R.; Merz, K. M. Jr; Onufriev, A.; Simmerling, C.; Wang, B.; Woods, R. J. The Amber biomolecular simulation programs. J. Comput. Chem., 2005, 26(16), 1668-1688

[95] Phillips, J. C.; Braun, R.; Wang, W.; Gumbart, J.; Tajkhorshid, E.; Villa, E.; Chipot, C.; Skeel, R. D.; Kalé, L.; Schulten, K. Scalable molecular dynamics with NAMD. J. Comput. Chem., 2005, 26(16), 1781-1802.

[96] Van Der Spoel, D.; Lindahl, E.; Hess, B.; Groenhof, G.; Mark, A. E.; Berendsen, H. J. GROMACS: fast, flexible, and free. J. Com-

put. Chem., 2005, 26(16), 1701-1718. [97] Rognan, D. Chemogenomic approaches to rational drug design. Br.

J. Pharmacol., 2007, 152(1), 38-52. [98] Klabunde, T. Chemogenomic approaches to drug discovery: similar

receptors bind similar ligands. Br. J. Pharmacol., 2007, 152(1), 5-7.

[99] Yang, S. Y. Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov. Today,

2010, 15(11-12), 444-450. [100] Dudek, A. Z.; Arodz, T.; Galvez, J. Computational methods in

developing quantitative structure-activity relationships (QSAR): a review. Comb. Chem. High Throughput Screen, 2006, 9(3), 213-228.

[101] Zou, J.; Xie, H. Z.; Yang, S. Y.; Chen, J. J.; Ren, J. X.; Wei, Y. Q. Towards more accurate pharmacophore modeling: Multicomplex-based comprehensive pharmacophore map and most-frequent-feature pharmacophore model of CDK2. J. Mol. Graph. Model., 2008, 27(4), 430-438.

[102] Horvath, D. In: Chemoinformatics and Computational Chemical Biology (Methods in Molecular Biology); Baiorath, Ed.; Humana Press: 2011; Vol. 672, pp. 261-98.

[103] Verma, J.; Khedkar, V. M.; Coutinho, E. C. 3D-QSAR in drug design - a review. Curr. Top Med. Chem., 2010, 10(1), 95-115.

[104] Eckert, H.; Bajorath, J. Molecular similarity analysis in virtual screening: foundations, limitation and novel approaches. Drug Dis-cov. Today, 2007, 12(5-6), 225-233.

[105] Sheridan, R. P. Chemical similarity searches: when is complexity justified? Expert Opin. Drug Discov., 2007, 2(4), 423-430.

[106] Willett, P. In: Annual Review of Information Science and Technol-ogy; Cronin Ed.; John Wiley & Sons, Inc: New York, 2009; Vol. 43, pp. 3-71.

[107] Maggiora, G. M.; Shanmugasundaram, V.; In: Chemoinformatics

and Computational Chemical Biology (Methods in Molecular Biology); Baiorath, Ed.; Humana Press: 2011; Vol. 672, pp. 39-100.

[108] Bender, A. How similar are those molecules after all? Use two descriptors and you will have three different answers. Expert Opin. Drug Discov., 2010, 5(12), 1141-1151.

[109] Bender, A.; Mussa, H. Y.; Glen, R. C. et al. Similarity searching of chemical databases using atom environment descriptors (MOL-PRINT 2D): evaluation of performance. J. Chem. Inf. Comput. Sci., 2004, 44(5), 1708-1718.

[110] Eckert, H.; Bajorath, J. Design and evaluation of a novel class-directed 2D fingerprint to search for structurally diverse active compounds. J. Chem. Inf. Model., 2006, 46(6), 2515-2526.

[111] Rush, T. S. 3rd; Grant, J. A.; Mosyak., L.; Nicholls, A. A shape-based 3-D scaffold hopping method and its application to a bacte-rial protein-protein interaction. J. Med. Chem., 2005, 48(5), 1489-1495.

[112] Haigh, J. A.; Pickup, B. T.; Grant, J. A.; Nicholls, A. Small mole-cule shape-fingerprints. J. Chem. Inf. Model., 2005, 45(3), 673-684.

[113] Eckert, H.; Bajorath, J. Determination and mapping of activity-specific descriptor value ranges (MAD) for the identification of ac-tive compounds. J. Med. Chem., 2006, 49(7), 2284-2293.

[114] Eckert, H.; Vogt, I.; Bajorath, J. Mapping algorithms for molecular similarity analysis and ligand-based virtual screening: design of DynaMAD and comparison with MAD and DMC. J. Chem. Inf. Model., 2006, 46(4), 1623-34.

[115] Willett, P. Chemical Similarity Searching. J. Chem. Inf. Comput.

Sci., 1998, 38(6), 983-996. [116] Melville, J. L.; Burke, E. K.; Hirst, J. D. Machine learning in vir-

tual screening. Comb. Chem. High Throughput Screen., 2009, 12(4), 332-343.

[117] Ma, X. H.; Jia, J.; Zhu, F.; Xue, Y.; Li, Z. R.; Chen, Y. Z. Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries. Comb. Chem. High Throughput Screen., 2009, 12(4), 344-357.

[118] Han, L. Y.; Ma, X. H.; Lin, H. H.; Jia, J.; Zhu, F.; Xue, Y.; Li, Z. R., Cao, Z. W; Ji, Z. L, Chen, Y. Z. A support vector machines ap-proach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor. J. Mol. Graph. Model., 2008, 26(8), 1276-1286.

[119] Liu, X. H.; Ma, X. H.; Tan, C. Y.; Jiang, Y. Y.; Go, M. L. et al. Virtual screening of abl inhibitors from large compound libraries by support vector machines. J. Chem. Inf. Model., 2009, 49(9), 2101-2110.

[120] Ma, X. H.; Wang, R.; Tan, C. Y.; Jiang, Y. Y.; Lu, T.; Rao, H. B.; Li, X. Y.; Go, M. L.; Low, B. C.; Chen, Y. Z. Virtual screening of selective multitarget kinase inhibitors by combinatorial support vector machines. Mol. Pharm., 2010, 7(5), 1545-1560.

[121] McGaughey, G.; Sheridan, R.; Bayly, C.; Culberson, C.; Kreatsou-las, C.; Lindsley, S.; Maiorov, V.; Truchon, J.-F.; Cornell, W. Comparison of topological, shape, and docking methods in virtual screening. J. Chem. Inf. Model., 2007, 47(4), 1504- 1519.

[122] Kitchen, D. B.; Decornez, H.; Furr, J. R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: methods and appli-cations. Nat. Rev. Drug Discovery, 2004, 3(11), 935-949.

[123] Wei, D. Q.; Zhang, R.; Du, Q. S; Gao, W. N.; Li, Y.; Gao, H.; Wang, S. Q.; Zhang, X.; Li, A. X.; Sirois, S.; Chou, K. C. Anti-SARS drug screening by molecular docking. Amino Acids, 2006, 31(1), 73-80.

[124] Barreiro, G.; Guimaraes, C. R. W.; Tubert-Brohman, I.; Lyons, T. M.; Tirado-Rives, J.; Jorgensen, W. L. Search for non-nucleoside inhibitors of HIV-1 reverse transcriptase using chemical similarity, molecular docking, and MM-GB/SA scoring. J. Chem. Inf. Model., 2007, 47(6), 2416-2428.

[125] Tikhonova, I. G.; Sum, C. S.; Neumann, S.; Engel, S.; Raaka, B. M.; Costanzi, S.; Gershengorn, M. C. Discovery of novel agonists and antagonists of the free fatty acid receptor 1 (FFAR1) using vir-tual screening. J. Med. Chem., 2008, 51(3), 625-633.

[126] Lin, T. W; Melgar, M. M.; Kurth, D.; Swamidass, S. J.; Purdon, J.; Tseng, T.; Gago, G.; Baldi, P.; Gramajo, H.; Tsai, S. C. Structure-based inhibitor design of AccD5, an essential acyl-CoA carboxy-lase carboxyltransferase domain of Mycobacterium tuberculosis. Proc. Natl. Acad. Sci. USA, 2006, 103(9), 3072-3077.

[127] Vidal, D.; Thormann, M.; Pons, M. A novel search engine for virtual screening of very large databases. J. Chem. Inf. Model., 2006, 46(2), 836-843.

[128] Mestres, J.; Knegtel, R. Similarity versus docking in 3D virtual screening Perspect. Drug Discovery Des., 2000, 20(1), 191-207.

[129] Bissantz, C.; Schalon, C.; Guba, W.; Stahl, M. Focused library design in GPCR projects on the example of 5-HT(2c) agonists: comparison of structure-based virtual screening with ligand-based search methods. Proteins, 2005, 61(4), 938-952.

[130] Tan, L.; Geppert, H.; Sisay, M. T.; Gutschow, M.; Bajorath, J. Integrating structure- and ligand-based virtual screening: Compari-son of individual, parallel, and fused molecular docking and simi-larity search calculations on multiple targets Chem. Med. Chem., 2008, 3( 10), 1566-1571.

[131] Handbook of the human genome project. Help me understand ge-netics. http://ghr.nlm.nih.gov/ (2011).

[132] Wu-Pong, S.; Shiang, R. In: Biopharmaceutical Drug Design and

Development; Wu-Pong, S. and Rojanasakul, Y., Ed.; Humana Press: Totowa, NJ, 2008; pp. 31-46.

[133] Bredel, M.; Jacoby, E. Chemogenomics: an emerging strategy for rapid target and drug discovery. Nat. Rev. Genet. 2004, 5, 262-275.

[134] Kubinyi, H.; Muller, G. Chemogenomics in Drug Discovery: A Medicinal Chemistry Perspective; Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, 2004.

[135] Harris, C. J.; Stevens, A. P. Chemogenomics: structuring the drug discovery process to gene families. Drug Discov. Today, 2006, 11(19-20), 880- 888.

Virtual Screening Strategies in Drug Discovery: A Critical Review Current Medicinal Chemistry, 2013, Vol. 20, No. 1 21

[136] Caron, P. R.; Mullican, M. D.; Mashal, R. D.; Wilson, K. P.; Su, M. S.; Murcko, M. A. Chemogenomic approaches to drug discov-ery. Curr. Opin. Chem. Biol., 2001, 5(4), 464- 470.

[137] Rognan, D. Chemogenomic approaches to rational drug design. Br. J. Pharmacol., 2007, 152(1), 38-52.

[138] Rognan, D. Structure-based approaches to target fishing and ligand profiling. Mol. Inf., 2010, 29(3), 176-187.

[139] Keiser, M. J.; Setola, V.; Irwin, J. J.; Laggner, C.; Abbas, A. I.; Hufeisen, S. J.; Jensen, N. H.; Kuijer, M. B.; Matos, R. C.; Tran, T. B.; Whaley, R.; Glennon, R. A.; Hert, J.; Thomas, K. L.; Edwards, D. D.; Shoichet, B. K.; Roth, B. L. Predicting new molecular tar-gets for known drugs. Nature, 2009, 462(7270), 175-181.

[140] Badrinarayan, P.; Sastry, G. N. Virtual high-throughput screening in new lead identification. Comb. Chem. High Throughput Screen-ing, 2011, 14(10), 840-860.

[141] Seifert, M. H.; Kraus, J.; Kramer, B. Virtual high-throughput screening of molecular databases. Curr. Opin. Drug Discovery

Dev., 2007, 10(3), 298-307. [142] Reddy, A. S.; Pati, S. P.; Kumar, P. P.; Pradeep, H. N.; Sastry, G.

N. Virtual screening in drug discovery -- a computational perspec-tive. Curr. Protein Pept. Sci., 2007, 8(4), 329- 351.

[143] Briansò, F.; Carrascosa, M. C.; Oprea, T. I.; Mestres, J. Cross-pharmacology analysis of G protein-coupled receptors. Curr. Top.

Med. Chem., 2011, 11(15), 1956-1963. [144] Mestres, J.; Gregori-Puigjane, E.; Valverde, S.; Sole, R. V. Data

completeness—the Achilles heel of drug-target networks. Nat. Bio-technol., 2008, 26(9), 983-984.

[145] Chen, X.; Lin, Y.; Gilson, M. K. The binding database: overview and user’s guide. Biopolymers, 2001, 61(2), 127-141.

[146] Chen, X.; Lin, Y.; Liu, M.; Gilson, M. K. The binding database: data management and interface design. Bioinformatics, 2002, 18(1), 130-139.

[147] Chen, X.; Liu, M.; Gilson, M. K. BindingDB: a Web-accessible molecular recognition database. Comb. Chem. High Throughput Screen., 2001, 4(8), 719-725.

[148] Wang, R.; Fang, X.; Lu, Y.; Yang, C. Y.; Wang, S. The PDBbind database: methodologies and updates. J. Med. Chem., 2005, 48(12), 4111-4119.

[149] Hu, L.; Benson, M. L.; Smith, R. D.; Lerner, M. G.; Carlson, H. A. Binding MOAD (mother of all databases). Proteins, 2005, 60(3), 333-340.

[150] Smith, R. D.; Hu, L.; Falkner, J. A.; Benson, M. L.; Nerothin, J. P.; Carlson, H. A. Exploring protein-ligand recognition with binding MOAD. J. Mol. Graph. Model., 2006, 24(6), 414-425.

[151] Okuno, Y.; Tamon, A.; Yabuuchi, H.; Niijima, S.; Minowa, Y.; Tonomura, K.; Kunimoto, R.; Feng, C. GLIDA: GPCR--ligand da-tabase for chemical genomics drug discovery--database and tools update. Nucleic Acids Res., 2008, 36(database issue), D907-D912.

[152] Krejsa, C. M.; Horvath, D.; Rogalski, S. L.; Penzotti, J. E.; Mao, B.; Barbosa, F.; Migeon, J. C. Predicting ADME properties and side effects: the BioPrint approach. Curr. Opin. Drug Discovery

Dev., 2003, 6(4), 470-480. [153] van der Horst, E.; Okuno, Y.; Bender, A.; Ijzerman A. P. Substruc-

ture mining of GPCR ligands reveals activity-class specific func-tional groups in an unbiased manner. J. Chem. Inf. Model., 2009, 49(2), 348-360.

[154] Klabunde, T. Chemogenomic approaches to drug discovery: similar receptors bind similar ligands. Br. J. Pharmacol., 2007, 152(1), 5-7.

[155] van Westen, G. J. P.; Wegner, J. K.; IJzerman, A. P.; van Vlijmen, H. W. T.; Bender, A. Proteochemometric modeling as a tool to de-sign selective compounds and for extrapolating to novel targets. Med. Chem. Commun., 2011, 2, 16-30.

[156] Geppert, H.; Vogt, M.; Bajorath, J. Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. J. Chem. Inf. Model., 2010, 50(2), 205-216.

[157] Bender, A.; Jenkins, J. L.; Scheiber, J.; Sukuru, S. C.; Glick, M.; Davies, J. W. How similar are similarity searching methods? A principal component analysis of molecular descriptor space. J. Chem. Inf. Model., 2009, 49(1), 108-119.

[158] Leach, A. R.; Gillet, V. J.; Lewis, R. A.; Taylor, R. Three-dimensional pharmacophore methods in drug discovery. J. Med.

Chem., 2010, 53(2), 539-558. [159] Moitessier, N.; Englebienne, P.; Lee, D.; Lawandi, J.; Corbeil, C.

R. Towards the development of universal, fast and highly accurate

docking/scoring methods: a long way to go. Br. J. Pharmacol., 2008, 153( Suppl. 1), S7- S26.

[160] Sanders, M. P. A.; McGuire, R.; Roumen, L.; De Esch, I. J.; de Vlieg, J.; Klomp, J. P. G.; de Graaf, C. From the protein’s perspec-tive: the benefits and challenges of protein structure-based pharma-cophore modeling. Med. Chem. Commun., 2011, 3, 28-38.

[161] Mestres, J.; Gregori-Puigjané, E.; Valverde, S.; Solé, R. V. The topology of drug-target interaction networks: implicit dependence on drug properties and target families. Mol. Biosyst., 2009, 5(9), 1051-1057.

[162] Morphy, R.; Rankovic, Z. Designed multiple ligands. An emerging drug discovery paradigm. J. Med. Chem., 2005, 48(21), 6523-6543.

[163] Hopkins, A. L. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol., 2008, 4(11), 682-690.

[164] Xing, L.; McDonald, J. J.; Kolodziej, S. A.; Kurumbail, R. G.; Williams, J. M.; Warren, C. J.; O'Neal, J. M.; Skepner, J. E.; Roberds, S. L. Discovery of potent inhibitors of soluble epoxide hydrolase by combinatorial library design and structure-based vir-tual screening. J. Med. Chem., 2011, 54(5), 1211-1222.

[165] Caporuscio, F.; Rastelli, G.; Imbriano, C.; Del Rio, A. Structure-based design of potent aromatase inhibitors by high-throughput docking. J. Med. Chem., 2011, 54(12), 4006-4017.

[166] Sager, G.; Ørvoll, E. Ø.; Lysaa, R. A.; Kufareva, I.; Abagyan, R.; Ravna, A. W. Novel cGMP efflux inhibitors identified by virtual ligand screening (VLS) and confirmed by experimental studies. J.

Med. Chem., 2012, 55(7), 3049 3057. [167] Spadaro, A.; Negri, M.; Marchais-Oberwinkler, S.; Bey, E.;

Frotscher, M. Hydroxybenzothiazoles as new nonsteroidal inhibi-tors of 17 -hydroxysteroid dehydrogenase type 1 (17 HSD1). PLoS One, 2012, 7(1), e29252.

[168] Lin, X.; Huang, X. P.; Chen, G.; Whaley, R.; Peng, S.; Wang, Y.; Zhang, G.; Wang, S. X.; Wang, S.; Roth B. L.; Huang, N. Life Be-yond Kinases: Structure-based discovery of sorafenib as nanomolar antagonist of 5-ht receptors. J. Med. Chem., 2012, 55(12), 5749 5759.

[169] Shivakumar, D.; Williams, J.; Wu, Y.; Damm, W.; Shelley, J.; Sherman, W. Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field. J. Chem. Theory Comput., 2010, 6(5), 1509-1519.

[170] De Cesco, S.; Deslandes, S.; Therrien, E.; Levan, D.; Cueto, M.; Schmidt, R.; Cantin, L. D.; Mittermaier, A.; Juillerat-Jeanneret, L.; Moitessier, N. Virtual screening and computational optimization for the discovery of covalent prolyl oligopeptidase inhibitors with activity in human cells. J. Med. Chem., 2012, 55(14), 6306 6315.

[171] Corbeil, C. R.; Englebienne, P.; Moitessier, N. docking ligands into flexible and solvated macromolecules. 1. Development and valida-tion of FITTED 1.0. J. Chem. Inf. Model., 2007, 47(2), 435-449.

[172] Sharma, R.; Lawrenson, A. S.; Fisher, N. E.; Warman, A. J.; Shone, A. E.; Hill, A.; Mbekeani, A.; Pidathala, C.; Amewu, R. K.; Leung, S.; Gibbons, P.; Hong, D. W.; Stocks, P.; Nixon, G. L.; Chadwick, J.; Shearer, J.; Gowers, I.; Cronk, D.; Parel, S. P.; O'Neill, P. M.; Ward, S. A.; Biagini, G.; Berry, N. G. Identification of novel antimalarial chemotypes via chemoinformatic compound selection methods for a high- throughput screening program against the novel malarial target, PfNDH2: increasing hit rate via virtual screening methods. J. Med. Chem., 2012, 55(7), 3144-3154.

[173] Warr, W. A. Scientific workflow systems: pipeline pilot and KNIME. J. Comput. Aided Mol. Des., 2012, 26(7), 801-804.

[174] Waltenberger, B.; Wiechmann, K.; Bauer, J.; Markt, P.; Noha, S. M.; Wolber, G.; Rollinger, J. M.; Werz, O.; Schuster, D.; Stuppner, H. Pharmacophore modeling and virtual screening for novel acidic inhibitors of microsomal prostaglandin E2 synthase-1 (mPGES-1). J. Med. Chem., 2011, 54(9), 3163-3174.

[175] Lavecchia, A.; Di Giovanni, C.; Pesapane, A.; Montuori, N.; Ragno, P.; Martucci, N. M.; Masullo, M.; De Vendittis, E.; Novel-lino, E. Discovery of new inhibitors of Cdc25B dual specificity phosphatases by structure-based virtual screening. J. Med. Chem. 2012, 55(9), 4142 4158.

[176] Chen, Y.; Fang, L.; Peng, S.; Liao, H.; Lehmann, J.; Zhang, Y. Discovery of a novel acetylcholinesterase inhibitor by structure-based virtual screening techniques. Bioorg. Med. Chem. Lett., 2012, 22(9), 3181-3187.

[177] Sutter, J.; Li, J.; Maynard, A. J.; Goupil, A.; Luu, T.; Nadassy, K. New Features that improve the pharmacophore tools from accelrys. Curr. Comput Aided Drug Des. 2011, 7(3), 173-180.

22 Current Medicinal Chemistry, 2013, Vol. 20, No. 1 Lavecchia and Di Giovanni

[178] Chen, Y.; Wen, D.; Huang, Z.; Huang, M.; Luo, Y.; Liu, B.; Lu, H.; Wu, Y.; Peng, Y.; Zhang, J. 2-(4-Chlorophenyl)-2-oxoethyl 4-benzamidobenzoate derivatives, a novel class of SENP1 inhibitors: Virtual screening, synthesis and biological evaluation. Bioorg. Med. Chem. Lett., 2012, 22(22), 6867-6870.

[179] Feng, J.; Jin, K.; Zhu, H.; Zhang, X.; Zhang, L.; Liu, J.; Xu, W. A novel aminopeptidase N inhibitor developed by virtual screening approach. Bioorg. Med. Chem. Lett., 2012, 22(18), 5863-5869.

[180] Rodrigues, T.; Moreira, R.; Gut, J.; Rosenthal, P. J.; O Neill, P. M.; Biagini, G. A.; Lopes, F.; dos Santos, D. J.; Guedes, R. C. Identifi-cation of new antimalarial leads by use of virtual screening against cytochrome bc1. Bioorg. Med. Chem., 2011, 19(21), 6302-6308.

[181] Daidone, F.; Montioli, R.; Paiardini, A.; Cellini, B.; Macchiarulo, A.; Giardina, G.; Bossa, F.; Borri Voltattorni, C. Identification by virtual screening and in vitro testing of human DOPA decarboxy-lase inhibitors. PLoS One, 2012, 7(2), e31610.

[182] Cao, R.; Liu, M.; Yin, M.; Liu, Q.; Wang, Y.; Huang, N. Discovery of novel tubulin inhibitors via structure-based hierarchical virtual screening. J. Chem. Inf. Model., 2012, 52(10), 2730 2740.

[183] Wang, F.; Li, J.; Sinn, A. L.; Knabe, W. E.; Khanna, M.; Jo, I.; Silver, J. M.; Oh, K.; Li, L.; Sandusky, G. E.; Sledge, G. W.; Nak-shatri, H.; Jones, D. R.; Pollok, K. E.; Meroueh, S. O. Virtual screening targeting the urokinase receptor, biochemical and cell-based studies, synthesis, pharmacokinetic characterization, and ef-fect on breast tumor metastasis. J. Med. Chem., 2011, 54(20), 7193-7205.

[184] Barreca, M. L.; Manfroni, G.; Leyssen, P.; Winquist, J.; Kaushik-Basu, N.; Paeshuyse, J.; Krishnan, R.; Iraci, N.; Sabatini, S.; Tabar-rini, O.; Basu, A.; Danielson, U. H.; Neyts, J.; Cecchetti V. Struc-ture-Based Discovery of Pyrazolobenzothiazine Derivatives As In-hibitors of Hepatitis C Virus Replication. J. Med. Chem., 2013,

56(6), 2270-2282. [185] Jansen, C.; Wang, H.; Kooistra, A. J.; de Graaf, C.; Orrling, K. M.;

Tenor, H.; Seebeck, T.; Bailey, D.; de Esch, I. J.; Ke, H.; Leurs, R. Discovery of Novel Trypanosoma brucei Phosphodiesterase B1 In-hibitors by Virtual Screening against the Unliganded TbrPDEB1 Crystal Structure. J. Med. Chem., 2013, 56(5), 2087 2096.

[186] Korb, O.; Stü tzle, T.; Exner, T. E. Empirical scoring functions for advanced protein ligand docking with PLANTS. J. Chem. Inf.

Model., 2009, 49(1), 84 96. [187] Lavecchia, A.; Di Giovanni, C.; Cerchia, C.; Russo, A.; Russo, G.;

Novellino, E. Discovery of a Novel Small Molecule Inhibitor Tar-geting the Frataxin/Ubiquitin Interaction via Structure-Based Vir-tual Screening and Bioassays. J. Med. Chem., 2013, 56(7), 2861-2873.

[188] Dominguez, C.; Boelens, R.; Bonvin, A. HADDOCK: a protein-protein docking approach based on biochemical or biophysical in-formation. J. Am. Chem. Soc., 2003, 125(7), 1731-1737.

[189] de Vries, S. J.; van Dijk, A. D. J.; Bonvin, A. M. WHISCY: What information does surface conservation yield? Application to data-driven docking. Proteins, 2006, 63(3), 479-489.

[190] Neuvirth, H.; Raz, R.; Schreiber, G. ProMate: A structure based prediction program to identify the location of protein-protein bind-ing sites. J. Mol. Biol., 2004, 338(1), 181-199.

[191] Petty, A.; Myshkin, E.; Qin, H.; Guo, H.; Miao, H.; Tochtrop, G. P.; Hsieh, J. T.; Page, P.; Liu, L.; Lindner, D. J.; Acharya, C.; MacKerell, A. D. Jr.; Ficker, E.; Song, J.; Wang, B. A small mole-cule agonist of EphA2 receptor tyrosine kinase inhibits tumor cell migration in vitro and prostate cancer metastasis in vivo. PLoS

One., 2012, 7(8), e42120. [192] Huang, H.; Yu, Y.; Gao, Z.; Zhang, Y.; Li, C.; Xu, X.; Jin, H.; Yan,

W.; Ma, R.; Zhu, J.; Shen, X.; Jiang, H.; Chen, L.; Li, J. Discovery and optimization of 1,3,4-trisubstituted-pyrazolone derivatives as novel, potent, and nonsteroidal farnesoid X receptor (FXR) selec-tive antagonists. J. Med. Chem., 2012, 55(16), 7037-53.

[193] Kirabo, A.; Embury, J.; Kiss, R.; Polgár, T.; Gali, M.; Majumder, A.; Bisht, K. S.; Cogle, C. R.; Keseru, G. M.; Sayeski, P. P. The stilbenoid tyrosine kinase inhibitor, G6, suppresses Jak2-V617F-mediated human pathological cell growth in vitro and in vivo. J.

Biol. Chem., 2011, 286(6), 4280-91. [194] Vallée, F.; Carrez, C.; Pilorge, F.; Dupuy, A.; Parent, A.; Bertin,

L.; Thompson, F.; Ferrari, P.; Fassy, F.; Lamberton, A.; Thomas, A.; Arrebola, R.; Guerif, S.; Rohaut, A.; Certal, V.; Ruxer, J. M.; Gouyon, T.; Delorme, C.; Jouanen, A.; Dumas, J.; Grépin, C.; Combeau, C.; Goulaouic, H.; Dereu, N.; Mikol, V.; Mailliet, P.; Minoux, H. Tricyclic series of heat shock protein 90 (Hsp90) in-hibitors part I: discovery of tricyclic imidazo[4,5-c]pyridines as po-tent inhibitors of the Hsp90 molecular chaperone. J. Med. Chem., 2011, 54(20), 7206-19.

[195] Howell, K. L.; De Vita, R. J.; Garcia-Calvo, M.; Meurer, R. D.; Lisnock, J.; Bull, H. G.; McMasters, D. R.; McCann, M. E.; Mills, S. G. Spiroimidazolidinone NPC1L1 inhibitors. Part 2: structure-activity studies and in vivo efficacy. Bioorg. Med. Chem. Lett., 2010, 20(23), 6929-32.

[196] Claffey, M. M.; Helal, C. J.; Verhoest, P. R.; Kang, Z.; Fors, K. S.; Jung, S.; Zhong, J.; Bundesmann, M. W.; Hou, X.; Lui, S.; Kleiman, R. J.; Vanase-Frawley, M.; Schmidt, A. W.; Menniti, F.; Schmidt, C. J.; Hoffman, W. E.; Hajos, M.; McDowell, L.; O'Con-nor, R. E.; Macdougall-Murphy, M.; Fonseca, K. R.; Becker, S. L.; Nelson, F. R.; Liras, S. Application of Structure-Based Drug De-sign and Parallel Chemistry to Identify Selective, Brain Penetrant, In Vivo Active Phosphodiesterase 9A Inhibitors. J. Med. Chem., 2012, 55(21), 9055 9068.

[197] Yang, J.; Wang, L. J.; Liu, J. J.; Zhong, L.; Zheng, R. L.; Xu, Y.; Ji, P.; Zhang, C. H.; Wang, W. J.; Lin, X. D.; Li, L. L.; Wei, Y. Q.; Yang, S. Y. Structural optimization and structure-activity relation-ships of N2-(4-(4-Methylpiperazin-1-yl)phenyl)-N8-phenyl-9H-purine-2,8-diamine derivatives, a new class of reversible kinase in-hibitors targeting both EGFR-activating and resistance mutations. J. Med. Chem., 2012, 55(23), 10685-99.

[198] Khanfar, M. A.; Hill, R. A.; Kaddoumi, A.; El Sayed, K. A. Dis-covery of novel GSK-3 inhibitors with potent in vitro and in vivo activities and excellent brain permeability using combined ligand- and structure-based virtual screening. J. Med. Chem., 2010, 53(24), 8534-45.

[199] Wei, L.; Wasilewski, E.; Chakka, S. K.; Bello, A. M.; Moscarello, M. A.; Kotra, L. P. Novel Inhibitors of Protein Arginine Deiminase with Potential Activity in Multiple Sclerosis Animal Model. J. Med. Chem., 2013, 56(4), 1715-1722.

Received: ????? Revised: ????? Accepted: ?????