Computer-Aided Drug Design of Bioactive Natural Products

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1780 Current Topics in Medicinal Chemistry, 2015, 15, 1780-1800

Computer-Aided Drug Design of Bioactive Natural Products

Veda Prachayasittikul1,3, Apilak Worachartcheewan1,2, Watshara Shoombuatong1, Napat Songtawee1, Saw Simeon1, Virapong Prachayasittikul3 and Chanin Nantasenamat1,3,

*

1Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol Univer-sity, Bangkok 10700, Thailand; 2Department of Clinical Chemistry, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand; 3Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand

Abstract: Natural products have been an integral part of sustaining civilizations because of their me-dicinal properties. Past discoveries of bioactive natural products have relied on serendipity, and these compounds serve as inspiration for the generation of analogs with desired physicochemical properties. Bioactive natural products with therapeutic potential are abundantly available in nature and some of them are beyond exploration by conventional methods. The effectiveness of computational approaches as versatile tools for facilitating drug discovery and development has been recognized for decades, without exception, in the case of natural products. In the post-genomic era, scientists are bombarded with data produced by advanced technolo-gies. Thus, rendering these data into knowledge that is interpretable and meaningful becomes an essential issue. In this re-gard, computational approaches utilize the existing data to generate knowledge that provides valuable understanding for addressing current problems and guiding the further research and development of new natural-derived drugs. Furthermore, several medicinal plants have been continuously used in many traditional medicine systems since antiquity throughout the world, and their mechanisms have not yet been elucidated. Therefore, the utilization of computational approaches and ad-vanced synthetic techniques would yield great benefit to improving the world’s health population and well-being.

Keywords: Natural products, Biological activity, Data mining, Drug discovery, Computer-aided drug design.

INTRODUCTION

Far-reaching impacts of natural products on human being have been noted for centuries in the realms of home reme-dies and medicines. Historical evidence of the first natural products was revealed through paleoanthropological studies in which pollen deposits were found in the grave of Shanidar in present-day Iraq, which is estimated to date back to more than 60,000 years ago [1]. The importance of natural prod-ucts to civilizations can be attributed to their diverse phar-macological properties. Medical records on the use of natural products as therapeutics have been documented across re-gions. Furthermore, a clay tablet depicting information re-garding medicinal extracts (i.e., resins, oils and juices from approximately 1,000 plants) was discovered in Mesopotamia and dates back to 2600 B.C. [2]. The Ebers Papyrus, an Egyptian medical text contained information on plant-based remedies for various diseases [3]. The first known Chinese text on this subject was called Wu Shi Er Bing Fang (con-taining 52 prescriptions), followed by Shennong Herbal (containing 365 drugs) and Tang Herbal (containing 850 drugs) [4]. As for western countries, historical evidence for the use of natural products was identified in monasteries of England, Ireland, Germany and France during the dark and middle ages [4]. Furthermore, it should not be overlooked

*Address correspondence to this author at the Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol Universi-ty, Bangkok 10700, Thailand; Tel: 66-2-441-4371; Fax: 66-2-441-4380; E-mail: [email protected]

that Avicenna, the Persian pharmacist, made significant con-tributions to the field of pharmacy through his work “Canon Medicinae” [4].

Historical records identified medicinal plants, fungi and algae as rich sources of bioactive natural products [5]. The use of medicinal plants originated with respect to the human instinct for survival, i.e., searching for food and seeking to avoid death [6]. Native Americans, used ashes of the plant genus Salvia to aid childbirth and protect infants from res-piratory diseases [7]. The ancient Europeans used Parmelia omphalodes extracts to cure burns and cuts due to its anti-inflammatory properties [8]. Fungi have been used as food (mushrooms), raw materials for perfumes and cosmetics, and ingredients for preparing alcohol and medicine since the ear-ly Chinese and Egyptian civilizations [9]. Fungi in the An-thozoans species, i.e., Chondrus crispus, were widely used for the treatment of chest infections [10]. Parmelia ompha-lodes (Linnaeus) Acharius were widely used in the British Isles as a dye and in Ireland as an anti-inflammatory agent to cure burns and cuts [11]. Among algae, the juice of the red alga Porphyra umbilicalis (Linnaeus) Kützing has been not-ed for its anticancer properties, particularly with respect to breast cancer [12].

The importance of natural products in medicine has been indicated by the continual use of classical natural products. One of the classic examples of a natural product is Papaver somniferum, the opium poppy, which contains naturally oc-curring alkaloids as bioactive compounds [13]. From the

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Computer-Aided Drug Design of Bioactive Natural Products Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18 1781

Egyptian to Chinese civilizations, opium was cultivated and used for several purposes. Ancient physicians used it as an anesthetic agent to perform medical surgery [14]. Likewise, they were used as painkillers during the American Civil War. In addition, they were used as recreational drugs in ancient China.

The Chinese and Indians are considered to be the pio-neers of herbal medicine, and their formulae have had great impacts on the traditional medicine of many countries worldwide [15]. The knowledge of the Chinese and Indians has been exchanged for a long time through the silk road [16]. Ayurveda is an Indian traditional medicine that defines the body in terms of three main constitutions (dosha), and the dynamic equilibrium of these dosha is essential for nor-mal bodily function [17]. In contrast, the disturbance of these dosha is believed to be the root causes of diseases [18]. Similarly, Traditional Chinese Medicine (TCM) defines yin, yang and qi as the three main biological forces in the human body. The balanced equilibrium of yin and yang is essential for being healthy, and qi is required as the energy that circu-lates and nourishes the entire body [19, 20]. Traditional Chi-nese medicine is considered to be the prototype of Japanese traditional medicine (kampo medicine) [21] and Korean tra-ditional medicine or Sasang constitution medicine (SCM) [22], to which the original formulae have been adapted. The Chinese and Ayurvedic traditional medicine systems have had great impacts on traditional medicine in Asian countries, including Thailand. The history of utilizing natural products for medicinal purposes has been noted since the Ayutthaya period (1350–1767 A.D.) [23].

Both Ayurveda and TCM are herbal medicine systems in which herb formulae that contain various medicinal herbs are prescribed to provide synergistic effects and reduce adverse effects [24]. Despite having distinct formulae, the traditional medicines of India and China are based on the same belief that an individual’s physical constitution plays a major role in susceptibility to diseases and its response to treatment [25]. The prescribed formulae can be adjusted according to the patient’s condition [24]. A similar basis of different body constitutions that lead to differential responses to herbs is also implied SCM [26].

The unique characteristics of these traditional medicines are in agreement with modern individualized medicine [27]. Furthermore, recent studies have revealed the relationships between traditional medicine systems (i.e., Ayurveda [28, 29], Chinese [30, 31], Japanese [32, 33] and Korean [34-36]) and genomic differences of individuals [27], which renders these systems thought-provoking alternative personalized treatment strategies in the post-genomic era [27].

The great importance of natural products in human being has been documented. Approximately 11% of drugs in the WHO’s essential medicines list are exclusively derived from plants, and 25% of the drugs prescribed worldwide are plant-derived products [37]. Most of the African and Asian popu-lations rely on traditional medicine for their primary healthcare [38] because of limited access to healthcare facili-ties and healthcare professionals [39], affordability and be-lief of safety [40]. In addition, the ancient use of natural products has formed the basis of later clinical, pharmacolog-ical and chemical studies [5], which can be identified from the discovery and development of many currently used drugs, e.g., aspirin, morphine, digitoxin, quinine and pilocar-pine [41].

Currently, the botanical statuses of countries differ be-cause of distinct features of advancement in science and technology, regulations within the country, culture and so-ciety [42]. In the Europe Union (EU) and the United States of America (USA), herbal extracts are used as active compo-sitions in herbal medicinal products, dietary supplements (in the USA) and food supplements (in the EU). In Asian coun-tries, natural products from plants are widely used as drugs for therapeutic purposes in traditional medicine and are used as health foods for the prevention of diseases and promotion of good health [42].

DRUG DISCOVERY AND DEVELOPMENT

Drug discovery and development is a complex process that requires expertise from multidisciplinary fields. It con-sists of many time consuming processes, from target identi-fication to clinical trials, that require substantial financial efforts (Fig. 1) [43]. According to the complexity of drug development processes, bioinformatics and computational

Fig. (1). Conceptual framework of drug discovery and development and the roles of computational approaches. (Hits = compounds that can bind to a target, Leads = hits with preferable potency, QSAR = quantitative structure-activity relationships, QSPR = quantitative structure-properties relationships).

Target identification

Hit identification

Hit-to-lead

Lead optimization

Pre-clinical trials

Clinical trials Phase I, II, III

Drug Approval Market

What biomolecule can be a target?

Which compounds can bind target ?

↑ Potency ↑ Drug-likeness

↓Toxicity

Data mining tools Molecular docking

Virtual screening Structural-based :

•  Molecular docking Ligand-based :

•  Pharmacophore •  Machine learning •  Similarity

QSAR QSPR

1782 Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18 Prachayasittikul et al.

approaches have become versatile tools for facilitating and accelerating drug design and development [44, 45]. The con-ceptual framework of drug discovery and development and the roles of computational approaches in each step are illus-trated in (Fig. 1).

Target identification is the process in which drug targets are identified [43] by databases that include experimental results. [43] The associated data mining tools are useful for creating databases, and molecular docking is capable of iden-tifying potential targets by docking drugs to large libraries of proteins [43]. Hits are defined as groups of compounds that exhibit desired activity in the screening process [43]. The process of hit identification can be performed using high throughput screening (HTS) and virtual screening. HTS is performed by screening an entire library of compounds against the target by automation; however, secondary assays are required for confirmation [46]. Virtual screening is an effective means of searching for potential compounds by using computational approaches. One widely used computa-tional method in this process is molecular docking. The crys-tal structure of the target protein is required to simulate bind-ing in silico against large libraries of compounds. Active compounds with good binding affinity to the target, repre-sented by a docking score [43], are identified as hits and will be further developed [47]. Hits are subsequently optimized to obtain improved potency and pharmacokinetic properties and reduced toxicity [43]. The optimization is performed by structural modification of compounds, where medical chem-istry and computational approaches play essential interactive roles [48]. Quantitative structure-activity relationships (QSAR) and quantitative structure-properties relationships (QSPR) are computational methods for correlating the chem-ical structures of the compounds with their activity /properties. Understanding these relationships is useful for structural modification by medicinal chemists in seeking for potential compounds [43, 48]. In addition, molecular model-ing and molecular docking can be used for the discovery of new binding sites on target proteins [43].

PRIVILEGED STRUCTURES

The similarity principle has been widely applied in drug design on the basis that structurally related compounds pos-sessing similar chemical structures may elicit similar biolog-ical activities [49]. In addition, the importance of most common molecular fragments or privileged structures has been noted by Evans et al. in 1988 [50]. Privileged structures are defined as molecular substructures that are capable of binding to a diverse array of receptors, and the modification of these substructures can provide an alternative approach to the discovery of novel receptor agonists and antagonists [50]. It also has been suggested that privileged structures provide affinity towards binding with receptors, whereas the rest of the molecule defines the selectivity of a potential compound [51]. Privileged structures have been successfully used as core structures for the synthesis of novel biologically active compounds [52-54] and as being a starting point for the synthesis of libraries [55]. The importance of privileged structures in drug design and discovery renders computa-tional approaches a powerful tool to address the search for novel privileged structures. It is widely known that natural products are rich sources of bioactive compounds. Recently,

diverse types of privileged structures have been identified from natural products, e.g., indole, quinolone, isoquinoline, purine, quinoxaline, quinazolinone, tetrahydroquinoline, tetrahydroisoquinoline, benzoxazole, benzofuran, 3,3-dimethylbenzopyran, chromone, coumarin, carbohydrate, steroid and prostanoic acid [55].

DRUG-LIKE PROPERTIES

Drug likeness is essential for effective drugs because ac-tive compounds become useless if they are not capable of behaving like drugs in clinical situations. Drug likeness is expressed by drug-like properties that are indicated by Lipinski’s rule of five [44, 56]. Lipinski’s rule suggests that drug-like compounds are molecules with molecular weights (MW) < 500 Da, calculated octanol/water partition coeffi-cients (clogP) < 5, a number of hydrogen-bond donors < 5 and a number of hydrogen-bond acceptors < 10 [56]. How-ever, these rules are used as guidelines rather than as abso-lute cut-offs for determining drug-like properties [44]. Re-cently, the importance of other physicochemical and struc-tural properties influencing drug-like properties has been suggested in terms of property-based design [57]. The basis of property-based design is that molecules with similar chemical structures are expected to possess similar pharma-cokinetic properties [57].

The pharmacokinetic profiles of drugs, i.e., absorption (A), distribution (D), metabolism (M), excretion (E) and toxicity (T), are essential for determining whether such bio-active compounds could be used as safe and efficient oral drugs [43], and they are considered to be crucial factors for decision-making in further development of the investigated compounds [58]. All of these ADMET properties indicate the drug likeness of compounds and notably affect efficacy, toxicity and drug-drug interactions [44]. For decades, many drugs have failed and been withdrawn in the late stages of drug development, causing considerable financial lost [43, 59]. The two main reasons that lead to the clinical failures of drugs are poor ADME properties [44] and severe toxicities (T) [43, 59]. Hence, considerable attention has been paid to the evaluation of the pharmacokinetic (ADME) properties and toxicity (T) of investigated compounds in the early stag-es of drug development to reduce the risk of failures and, therefore, save time and cost [60, 61]. In this regard, many computational approaches have been employed for the pre-diction of ADMET properties [62-67].

COMPUTATIONAL TOOLS

Databases

In recent years, we have witnessed the introduction of a wide range of databases to aid drug discovery efforts, and these can be broadly classified into two groups: bioactivity databases and target databases.

Bioactivity databases are valuable tools for identifying hit chemical compounds. For example, the ChemNavigator database (http://www.chemnavigator.com/) is a comprehen-sive database because it contains over 91.5 million druggable compounds, although post-curation is needed before per-forming docking studies and/or quantitative structure-activity relationship (QSAR) studies [68]. ZINC is a dock-


ing-studies friendly database (http://zinc.docking.org) be-cause 3D formatted and purchasable 35 million drug-like compounds are deposited [69]. The ChEMBL database (https://www.ebi.ac.uk/chembl/) contains over 1 million compounds with information on their binding affinities, functional assays, bioactivity measurements and ADMET properties [70]. Pubchem is a freely accessible repository that contains more than 63 million compounds and provides diverse bioactivity results for approximately 45 million. One of the features that makes Pubchem an attractive tool for in silico drug design is the PubChem Download Service [71]. Binding DB is a public and openly accessible database that has approximately 20,000 binding affinities of small com-pounds that have been experimentally tested with known 3D structural available protein targets.

Target databases are important for identifying druggable proteins that are involved in pathogenesis. For instance, the tropical disease pathogens target database (http://tdrtargets. org) contains information on protein structures, functional genomics and biochemical pathways to aid the in silico iden-tification of protein targets [72]. The potential drug target database (http://www.dddc.ac.cn/pdtd/) contains over 1,100 3D druggable protein structures ranging from enzymes to lipid binding proteins [73]. The Therapeutic Target Database (http://xin.cz3.nus.edu.sg/group/ttd/ ttd.asp) contains over 2,360 targets with information on 3D structures, diseases, binding properties and functional properties [74]. The Pro-

tein Data Bank (PDB) contains all known crystallized 3D protein structures, conveniently providing new information (structural information that is not provided in the sequence database, e.g., GeneBank) and tremendously aiding in silico drug design by allowing researchers to identify novel poten-tial drug targets and to perform docking studies [75].

Chemical Space of Natural Products

Chemical space is the total possible number of de-scriptors from chemical compounds. Similar to the spatial extent of space the universe, these descriptors are infinite in number. Despite the advancement in the synthesis of organic compounds and the characterization of nature products, only a small fraction of compounds have been synthesized and used. Thus, by exploring the origin of chemical space in liv-ing organisms, new strategies to combat diseases will emerge. Visualization of the chemical space of natural prod-ucts obtained from 12 natural product databases available from the ZINC database is shown in (Fig. 2) by means of a PCA scores plot.

Chemical space analyses of FDA approved drugs were performed to explore the properties and characteristics of drug-like chemical compounds. For example, Vieth et al. [76] studied fragment analysis of 1,082 FDA approved drugs and 1,729 marketed drugs. The results showed that the halo-gen contents of marketed drugs are identical, and the molec-

Fig. (2). PCA plot of compounds from 12 databases obtained from the ZINC database. Random selection of 100 compounds from each of the 12 databases was carried out followed by representing each compound by the ECFP substructure fingerprint. Finally, PCA was computed in R using the prcomp function from the stats package and the resulting plot is visualized using the ggplot2 package. Acronyms and full names of the 12 databases are provided hereafter (AfroDb: African natural products, AnalyticCon: AnalytiCon discovery natural products, HIT: Herbal Ingredients Targets, IBScreen: IBScreen natural products, Indofine: Indofine natural products, NPACT: Naturally occurring plant based anticancerous compound-activity-target database, Nubbe: Nuclei of Bioassays, Biosynthesis and Ecophysiology of Natural Products, Princeton: Princeton natural products, Specs: Specs natural products, TCM: Traditional Chinese Medicine Database, Tongju: Tongji Univer-sity herbal ingredients in vivo metabolism, UEFS: Universidade Estadual De Feira De Santana natural products).

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AnalyticCon

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IBScreen

Indofine

NPACT

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Princeton

Specs

TCM

Tongju

UEFS


ular weights of FDA approved drugs are lower than 500. These results were consistent with Lipinski's rule of five, which claims that drugs should possess a MW smaller than 500 to have good bio-absorption and bioavailability. Chemi-cal space analyses were performed on natural products as well. For instance, Ganesan [77] used 24 unique natural products to understand the associated chemical space. Of all the 24 natural products, half of them obey Lipinski’s rule of five, whereas the other half disobey the rule. A closer exam-ination of the physicochemical properties of these 24 natural products revealed that almost all of them obey the log P rule, such that their values are smaller than 5.

Koch et al. [78] explored the chemical space of natural products by classifying their chemical scaffold, which al-lowed the identification of 11 novel ß-hydroxysteroid dehy-drogenase type 1 inhibitors. Reayi and Arya [79] stressed that the chemical space of natural products can be populated by diversity-oriented synthesis (DOS), a strategy in chemical synthesis to quickly create a library of compounds, which will aid in the deorphanization of druggable protein targets. Josefin et al. [80] utilized ChemGPS-NP to explore the chemical space of natural products from several databases and found that 40,348 compounds from the Dictionary of Nature Products Database passed the Lipinski’s rule of five. Osada and Hertweck [81] claimed that the chemical space of natural products is populated naturally by gene clustering, where gene natural product synthesizer enzymes are altered to increase their chemical space. Lachance et al. [82] claimed that the bioactivity of the chemical space of natural products can be analyzed, charted and navigated to identify relevant substituents to aid modern chemical synthesis in drug discovery and development.

Analogous to the Linpiski’s rule of five (drug-likeness), Zhou et al. [83] used structure-activity relationships to ex-plore the chemical space of natural products to define “bio-active natural compound-likeness” (BNC-likeness). Struc-tural properties were compared between bioactive and non-bioactive natural products and between the drug-likeness and BNC-likeness models. A dataset of 1,580 natural products was obtained from a total of 7,549 natural product ingredi-ents from the Ethnobotanical Database and Dr. Duke’s Phy-tochemical Database. Of 1,580 natural products, 790 were bioactive whereas 790 were not, providing a balanced da-taset. SVM with radial basis function kernels was used to perform bioactive natural compound-likeness models, using 1,580 compounds with bioactivity as the training set. The performance of the models was tested with an independent external data set that included 81 bioactive natural products and 81 non-bioactive natural products from widely used me-dicinal herbs. The prediction results demonstrated that 75 bioactive compounds were successfully classified, suggest-ing that the models are robust and do not have the problem of overfitting. Overfitting is one of the problems in machine learning and occurs when noise data are incorporated as in-dependent variables to develop highly predictive models. Although these models work very well with internal datasets, their performance is very low when a new class of data or a test set is applied [83].

A closer examination of the structural properties between bioactive and non-bioactive natural products showed that

they were clearly different. For example, for the structural properties of bioactive natural products, the molecular weights, the number of rings, the number of carbon atoms and the number of oxygen atoms, in particular, were higher than those of non-bioactive natural products [83]. In contrast, the results showed that most of the bioactive natural products exhibited drug likeness despite having increased numbers of hydrogen bonds donors and acceptors. This result suggested that natural products have desirable properties in drug dis-covery and development because compounds that obey the rule-of-five are orally active and very specific in binding to their targets [83].

To compare the drug-likeness and BNC-likeness models, a data set of 59,000 drugs from the World Drug Index (WDI) were randomly chosen and screened to obtain 3,930 com-pounds, of which 1,965 were bioactive and 1,965 were non-bioactive. Molecular descriptors were extracted for each compound to develop a drug-likeness model using SVM as the learning technique. The performance of the drug-likeness model decreased when the natural product data set was used, and the opposite phenomenon was observed for the BNC-likeness model [83]. These two models may have differed because they rely on different properties of synthesized drugs and natural products. A closer look at the key de-scriptors of these two models revealed by the RuleSet algo-rithm, an algorithm that is based on a decision tree algo-rithm, indicated only a few important descriptors to perform the classification. In the development of the BNC-likeness predictive model, 180 descriptors were used, whereas 328 descriptors were used as inputs to construct the drug-likeness model. There were significant differences when the distribu-tions of the 180 and 328 descriptors were plotted. To con-firm these differences, 1,647 descriptors were extracted from Dragon based on molecular descriptors for each compound and were split with the k-means clustering approach. The descriptors were clustered into 50 groups based on their Pearson’s correlation coefficients. The important descriptors for both models (i.e., drug-likeness and NBC-likeness) were significantly different because the descriptors in clusters 35, 33, 28, and 36 were mainly used to build the BNC-likeness prediction model, and they were rarely used to create drug-likeness models. In contrast, clusters 19, 7, and 18 were largely used to build drug-likeness models and were rarely used to make BNC-likeness models [83].

Natural Products as Sources of Inspiration for New Drugs

Small molecules and secondary metabolites have been economically designed and synthesized by nature for the benefit of evolution; in other words, they have been evolu-tionarily selected [84]. Regarding the power of evolution, natural products contain diverse types of biologically rele-vant privileged structures that have saved millions of lives, which renders them a continuous source of inspiration for the discovery of new drugs [85]. These naturally occurring ligands serve as excellent structural starting points for ex-ploring biologically relevant chemical space [86]. Therefore, the identification of natural products that are capable of modulating protein functions in pathogenesis-related path-ways is the heart of drug discovery and development [78]. Until now, distinct natural products have been chemically


modified and driven to become Food and Drug Administra-tion (FDA) approved drugs [77]. From 1981 to 2010, natural products and their derivatives accounted for 74.8% of all candidate drugs approved by the FDA [87]. Good examples of natural product-inspired drugs are carfilzomib, omacet-axine mepesuccinate and mitoxantrone.

Carfilzomib is a natural-linked compound derived from a naturally occurring bacterial proteasome inhibitor, epox-omicin. Carfilzomib was first synthesized in 1992 by Hanada et al. [88]. However, the mechanism of action of this com-pound was unknown. In the late 1990s, carfilzomib was structurally modified by Crews’ lab from Yale University to obtain a derivative that was structurally similar to the parent compound, epoxomicin [89, 90]. Several research groups put forward great effort to structurally modify carfilzomib. Eventually, a derivative of carfilzomib was obtained by Pro-teolix and Onyx and was approved by FDA in 2012 for the treatment of multiple myeloma [91].

Homoharringtonine is a bioactive cephalotaxine alkaloid isolated from the extract of evergreen trees. In 1976, homo-harringtonine was clinically observed for its anticancer po-tential against acute leukemia [92]. Since then, this natural compound has been examined across several organizations and companies. Finally, the ester derivative of homoharring-tonine was approved by the FDA in 2012 for the treatment of chronic myeloid leukemia under the name omacetaxine mepesuccinate [91].

Mitoxantrone is an anticancer agent derived from the natural product pharmacophore. Mitoxantrone is a doxorubi-cin analog that was designed to minimize cardiotoxicity of its parent compound [93]. Mitoxantrone has been approved by the FDA for the treatment of many cancers, including acute leukemia, breast cancer and lymphomas [94]. In addi-tion, it was approved by the European Medicine Agency (EMEA) of the European Union (EU) in September 2012 for the treatment of B cell lymphomas [91]. At present, there are applications of this drug before the FDA for approval for the treatment of non-Hodgkin’s lymphoma [91].

Finally, the commercial success of these natural-derived drugs clearly demonstrates that natural products provide great sources of biologically relevant privileged structures that are useful as structural starting points for the screening, design and development of novel potential drugs.

Synthesis of Natural Products

Natural products are in high demand owing to their ex-ceptional range of bioactivities. Some natural products are limited or inaccessible in nature. Organic synthesis often solves this problem by supplying these scarce compounds and enabling the conversion of bioactive natural compounds into more drug-like derivatives [95]. It is well known that the chemical structures of the majority of natural products are complex, which renders their total synthesis a difficult task [95]. Therefore, novel organic synthetic approaches have been developed in an attempt to yield potential compounds with medicinal value [96]. Principally, structural modifica-tions of the natural product core structures are performed to improve selectivity and potency, to provide additional prop-erties [97], and to facilitate their synthesis [95]. Furthermore,

some novel synthetic strategies have been developed to in-crease structural diversity, in other words, to expand the chemical space of investigated compounds [84, 98]. Exam-ples of organic synthesis methods are given below.

Semi-synthesis is performed by the chemical modifica-tion of natural products to improve potency, selectivity and other properties [97]. This method has been historically used to yield a number of therapeutic compounds or compounds with significant impacts on mankind. A notable example of this approach is heroin, which is derived from the acetylation of morphine [99].

Fragment exchange is a complementary approach that re-places chemical fragments of natural products with syntheti-cally derived fragments [97]. Statin and its derivatives, i.e., mevastatin, lovastatin, simvastatin and atorvastatin, are drugs that lower the concentration of lipids. These com-pounds have been developed from naturally occurring statin based on the semi-synthesis and fragment exchange methods [100].

Diversity-oriented synthesis (DOS) is an effective tool to achieve a library of structurally diverse compounds with desirable biological properties [101, 102]. Structural diversi-ty is one of the key strategies for expanding the investigated chemical space and thereby increasing the rate of finding potential hits [98]. Conceptually, natural products are used as starting scaffolds to generate compound libraries by various organic synthesis methods [103], in which novel molecules are generated in short reaction sequences (not more than 4 or 5 steps) [104]. Examples of natural products used as starting scaffolds are gibberellic acid (a plant hormone), adrenos-terone (steroid hormone) and quinine (isolated compound from the bark of the cinchona tree) [103].

Function-oriented synthesis (FOS) is an effective strategy for producing therapeutic lead compounds in a step-economical fashion [95] such that small molecules are gen-erated with less structural complexity and with preferable properties [95]. The principle of FOS is based on the fact that only a portion (substructure) of a compound is responsi-ble for its biological activities, and these crucial moieties can be modified to facilitate synthesis, enhance desirable biolog-ical activities and improve drug-like properties [95]. It should be noted that natural products are most likely bind to multiple targets [84], and they are not designed for human therapeutic use [95]. These characteristics lead to undesired side effects and inferior pharmacokinetic properties [95]. The benefits of FOS have been noted to address these prob-lems by reducing undesired side effects, enhancing desired biological activities and improving pharmacokinetic proper-ties [95]. FOS has been applied for the development of many natural compounds, such as bryostatin [105], halichondrin B [106], statin [107], dynemicin [108] and laulimalide [95].

One of the challenges in drug discovery and development is the identification of biologically relevant areas that are located inside an investigated chemical space [109]. Biolo-gy-oriented synthesis (BIOS) is based on the structural anal-ysis of small molecules and target proteins, where biological relevance is a prime criterion for the selection of starting scaffolds for the synthesis of biologically active compound collections [84]. Briefly, natural product scaffolds are ana-


lyzed and classified according to their core structures, and protein targets are clustered by their similarity. Consequent-ly, scaffold collections and protein clusters are matched by biological pre-validation [84] to provide a starting point for the subsequent synthesis of small molecules enriched with biological activity [86]. In this regard, computational ap-proaches, i.e., chemoinformatics and bioinformatics, are nec-essary [86]. It should be noted that BIOS only provides a starting point for discovery, and the continuous development of practical synthetic methods, i.e., one-pot sequences, cas-cade and domino reactions, are essential as a final step to obtain biologically active, naturally derived compound col-lections [86].

Until now, many synthetic strategies have been reported for the synthesis of natural product analogs, including the solid-phase technique [110, 111], solution-phase technique [96, 111], polymer-immobilized scavenger reagents [112, 113], direct sorting [114], combinatorial biosynthesis [115-118], total synthesis using gold catalysis [119] and biology-oriented synthesis [84, 120, 121].

Quantitative Structure-Activity/Property Relationship (QSAR/QSPR)

Quantitative structure-activity/property relationships (QSAR/QSPR) describe mathematical and statistical rela-tionships between molecular descriptors of compounds (X) and their biological activities/properties (Y). Hansch et al. first demonstrated the use of mathematical and statistical approaches for constructing a QSAR/QSPR model [122, 123]. Over the last several decades, the QSAR/QSPR model has been used to effectively reduce time-consuming, labori-ous and expensive process in innovation drug research [124-126], and it has also performed well for the prediction of physicochemical and biological properties [127-135]. Thus, it is desirable to develop an efficient and reliable QSAR/QSPR model to improve the drug discovery process. The development of a QSAR/QSPR model is essentially comprised of five major steps: i) calculating the molecular descriptors; ii) selecting relevant and informative molecular descriptors; iii) dividing the data into training/internal and testing/external sets; iv) establishing the QSAR/QSPR model using the training set; and v) validating the QSAR/QSPR model.

Calculating the Molecular Descriptors

The chemical structure of a compound can be represented as a set of numerical values, called molecular descriptors [136]. First, chemical structures are drawn, geometrically optimized and calculated to obtain descriptor values. Typi-cally, many types of descriptors, i.e., physicochemical prop-erties, molecular properties and molecular fingerprints, can be extracted from the chemical structure of natural products using computer software [137]. Although several thousand descriptors can be obtained from conventional software packages, those descriptors may not be informative or useful for predicting the bioactivity of compounds of interest. Thus, feature selection via machine learning algorithms is essential to select a set of informative descriptors prior to the con-struction of QSAR/QSPR models [125]. Bioactivities are considered to be the effects of the natural products on the living organisms, which can be either beneficial or harmful,

depending on their structural composition and concentration. Accurate and precise bioactivity data are essential for the successful construction of QSAR/QSPR models. Therefore, multiple rounds of activity assays should be performed to obtain accurate and precise bioactivities. Recently, QSAR models were successfully constructed using several bioactiv-ities, such as minimum inhibition, toxicity, solubility, sorp-tion, absorption, bioconcentration, permeability, metabolism, clearance and binding affinity [125]. Initially, the chemical structures of the natural products can be collected from pub-lic databases, commercial repositories and the literature. Chemical structures are drawn, geometrically optimized and subjected to descriptor calculations [125]. Many types of descriptors (e.g., constitutional, topological, geometric, elec-trostatic, fingerprints, steric, quantum chemical descriptor) can be obtained independently from the software that is used to perform the descriptor calculation. The physicochemical properties, quantum chemical properties and molecular fin-gerprint properties of the natural products can be extracted as a set of descriptor values by free and/or commercial software [138-140]. There are openly available descriptor calculators that permit descriptor extractions for the user. For example, the free online E-Dragon molecular descriptors calculator (http://www.vcclab.org/lab/edragon/start.html) allows users to extract 1,600 molecular descriptors, where SDF (MDL) or MOL2 (Sybl) input files of the 3D structures (with added hydrogen atoms) are used as inputs to extract the descriptors [141]. Another example of a free descriptor calculator is Jcompoundmapper (http://jcompoundmapper.source forge. net/); users can download the java client for this application, called JCMapperCLI.jar, and conveniently calculate molecu-lar fingerprint descriptors using the Command Prompt script [142]. In addition, molecular structures that represented as simplified molecular-input line-entry system (SMILES) for-mat together with endpoint or their biological and chemical properties can use for development of QSPR/QSAR models by Monte Carlo method in CORrelation And Logic (COR-AL) software (http://www.insilico.eu/ coral) [134].

Selecting Relevant and Informative Molecular Descriptors

Many QSAR/QSPR models are not suitable to handle a large number of irrelevant descriptors. Thus, the selection of relevant/informative descriptors plays a crucial rule in the construction of QSAR/QSPR models. The objectives of se-lecting descriptors based on importance are manifold: (I) to alleviate overfitting and enhance QSAR/QSPR performance; (II) to provide faster and more cost-effective models; and (III) to gain deeper insight into the underlying chemical structures of natural products [143]. Currently, there are three major methods of feature selection: filter, wrapper, and embedded approaches [143, 144]. Filter approaches assess the relevance of descriptors by ranking a feature relevance score and filtering a feature relevance score, such as the t-test. Subsequently, the top-ranked informative features are used to construct a predictive model. This approach consid-ers the intrinsic properties of the data and ignores the interac-tion with the model. Filter techniques are simple and fast, with little computational complexity, and they are also easy to manipulate for very high dimensional data sets. However, these techniques are independent of the prediction model. Instead of focusing on gaining an informative feature inde-pendently of the model selection step, wrapper approaches


were proposed to mitigate this issue by embedding the model within the candidate feature subset. Some examples of wrap-per approaches include sequential forward selection (SFS) [145], sequential backward elimination (SBE) [145] and ge-netic algorithm [146]. Advantages of the wrapper approaches include their interaction between the candidate feature subset and the model selection, whereas a common drawback of these techniques is that they have a higher risk of overfitting than filter techniques and are computationally intensive. In the last category of feature selection techniques, termed em-bedded approaches, the selection of the informative subset features is built into the model. Similar to wrapper approach-es, embedded approaches have the advantage that they can include the interaction with the classification model; howev-er, they are far less computationally intensive than wrapper methods. Some examples of embedded approaches included decision tree [147], logistic model tree [148] and random forest approaches [149]. (Table 1) provides a summary of the feature selection methods, showing the most prominent ad-vantages, disadvantages, and some examples for each meth-od.

Dividing the Data into Training and Testing Sets

To alleviate the overfitting problem, a QSAR/QSPR model must perform well on both training and testing sets to be an effective and efficient model. Currently, there are a number of splitting algorithms, such as Kennard and Stone, Dublex and k-means sampling. These three algorithms were implemented with the R program within the prospectr soft-ware package. An introduction to the prospectr software package can be downloaded at no cost from http://cran.r-project.org/web/packages/prospectr/index.html.

Establishing the QSAR/QSPR Model

The construction of a QSAR/QSPR model is based on the principal idea of machine learning. Currently, a few well-known QSAR/QSPR models based on machine learning techniques include multiple linear regressions (MLR), partial least square (PLS), k-nearest neighbor (k-NN), artificial neu-ral network (ANN), support vector machine (SVM), decision tree (DT), and random forests (RF). All of these methods have been reported in many applications of QSAR/QSPR modeling. Machine learning tasks are typically classified into two broad categories consisting of classification and regression tasks. Classification tasks aim to discriminating a variable Y into its class or property, where the Y variable could be classified into two and more than two classes, which are called binary and multi-class classification, re-spectively. In contrast, the regression task primarily focuses on predicting the value of the variable Y with a numerical output. The MLR, PLS, ANN, SVM, and RF methods can be utilized in both classification and regression tasks, whereas k-NN and DT are used only in the classification task. Addi-tionally, the tasks of machine learning could be further di-vided according to their inclusion (supervised learning) or omission (unsupervised learning) of the variable Y. All ex-amples of QSAR/QSPR models are commonly used in su-pervised learning tasks, whereas a well-known unsupervised learning method is principal component analysis (PCA). (Fig. 3) displays the schematic representation of the major

concepts of popular machine learning techniques that are used for construction of QSAR/QSPR models.

A simple method involving a classification task is the k-NN algorithm. This algorithm is conceptually based on a distance function, such as the Euclidean distance, to measure the similarity between a pair of data. Given a data set

NxxD ,...,1= , where Njx ℜ∈ and N is the number of mo-

lecular descriptors, a positive integer k, and a new datum x to be classified, the k-NN algorithm finds the k nearest neighbors of x in D, denoted as k-NN( x ), and returns the dominating class label in k-NN( x ) as the label of x . Given

descriptors of two compounds (e.g., ix and jx ), the Euclide-

an distance ),( ji xxDist is

i

N

njninji xxxxDist ∑

=

−=1

2)(),(

(1)

where inx is the ith compound with the nth molecular de-

scriptor.A schematic representation the k-NN method is il-lustrated in (Fig. 3A).

The MLR method attempts to model the relationship and behavior between a set of molecular descriptors X and a quantitative value Y by fitting a linear equation to observed data. In MLR analysis, stepwise regression is used to select the most informative descriptor and improve the perfor-mance of the QSAR/QSPR model. Formally, the QSAR/QSPR model constructed from the MLR method is

01

22110 ... ββββββ +=++++= ∑=

N

ninniNNiii xxxxy (2)

where iy is the output value. To obtain the MLR parameter

iβ , the ordinary least squares (OLS) approach is used by

minimizing the sum of the actual and predicted values to give a loss function (actual value – predicted value).

In practice, it is laborious to directly manipulate and vis-ualize high-dimensional data. Rather than analyzing the orig-inal dimension of data X, the importance of the extracted variable is more useful. In this regard, PCA is likely the most popular unsupervised learning technique based on a statisti-cal approach that reduces the dimensionality of the data set to a smaller subset known as principal components (PCs) while preserving its dominant characteristics (variance) [150]. The major goals of PCA are as follows: 1) to extract the most information from X variable; 2) to analyze the pat-tern of X and Y variables; and 3) to remove an outlier(s). (Fig. 3B) shows the scores and loading plots derived from PCA approach.

Practically, if we have more variables (i.e., molecular de-scriptors) than compounds, the MLR method will not be a suitable option. Further, the OLS approach might provide an

unstable parameter iβ , which is difficult to interpret. The

PLS method was proposed to solve a large number of varia-bles. This approach is the most commonly utilized approach,


Table 1. Summary of feature selection approaches.

Model Search Advantage Disadvantage Examples

Filter -Independent of the classifier

-Better computational complexity than wrapper methods

-Ignores interaction with the classifier -t-test

Wrapper -Interacts with the classifier -Model feature dependencies

-Risk of overfitting -Classifier dependent selection

-Genetic algorithma -Sequential forward selectionb

-Sequential backward selection b

Embedded -Better computational complexity than wrapper

methods -Interacts with the classifier

-Model feature dependencies

-Classifier dependent selection -Decision tree c

-Logistic model tree d

-Random forests e

a Reference [146], b Reference [145], c Reference [147], d Reference [148], e Reference [149].

Fig. (3). Schematic overview of commonly used machine learning techniques comprising of k-nearest neighbor (A), Principal component analysis (B), artificial neural network (C), support vector machine (D), decision tree (E), and random forests (F).

rather than MLR or PCA. Practically, PLS (projection to latent structures), is used to establish the correlation of a matrix of X variables that have high variance and good corre-lation with a matrix of Y variables. The correlation approxi-mation is achieved by simultaneously projecting the X and Y matrices on lower dimensional spaces that are represented by PLS components. The idea behind the PLS model is to cal-

culate the PLS component T by decomposing the block of X = TP + residuals and predict the response variable Y = TC + residuals, where P and C are the inverse of loading scores of X and Y, respectively. Additional details of the PLS model can be found in references [151-153].

The k-NN, MLR, and PLS methods are suitable for mod-eling linear relationships between the variables X and Y;


thus, when data sets possess a nonlinear relationship, these three methods might not perform well. ANN was proposed for use with nonlinearly separable data sets. Computational models of this method were inspired by the human central nervous system. The details of ANN evolved from the per-ceptron concept, which is one of algorithms used for super-vised classification [154]. Mathematically, ANN is repre-sented by a nonlinear weighted sum:

)( 01

ββθ += ∑=

N

ninni xy (3)

where )(•θ is the activation function. The sigmoid func-

tion )(•θ is a commonly used activation function in ANN

and refers to the special case of the logistic function defined

by

011

1)(

ββθ

+∑=+

= N

ninnx

eX

(4)

The prediction result possesses a value of 1 if Eq. 4 is greater than the threshold value; otherwise, the prediction re-sult is 0. Because the goal of any supervised learn-ing algorithm is to construct a model that performs well on both internal and external sets, backpropagation, also called backward propagation, is a commonly used method for train-ing ANN by using an optimization method such as gradient descent. This method calculates the gradient of a loss function with respect to all the weights or parameters iβ in the net-

work. The gradient is fed to the optimization procedure to provide more accurate weights and to minimize the loss func-tion. This method has been applied in both classification and regression tasks. (Fig. 3C) shows the most common structure of ANN composing of three layers, i.e. input, hidden, and out-put layers, with full inter-connection (Table 2). Additional details of the ANN method can be found in references [155] and [156].

SVM was originally developed for classification by Cor-tes and Vapnik [157]. This method attempts to construct a separating hyperplane that maximizes the margin between the two classes of data sets. Intuitively, a good separation or classification occurs when the hyperplane has the greatest distance to neighboring data points of both classes because a larger margin leads to lower values of the loss function of the classifier and also accurately predicts each data point. To easily understand SVM, a linear model (i.e., Eq. 2) can be used for a binary classification problem given a data set D. To achieve the maximizing margin, the optimization ap-proach is defined as

2

, 2

1min ww β

(5) 1)(.. 0 >+ ββ iii xyts

This method has β,w as its parameters. Previously,

SVM has been successfully applied in QSAR modeling by utilizing the �-insensitive loss function [157, 158] as fol-lows:

Lε (y, f (x,β))={|y− f (x,β)|−ε ,y− f (x,β)|≥ε

0 ,y− f (x,β)|<ε

(6)

where y is the actual value, ),( βxf is the predicted value

(in which the simple form is )(xf , where

nxxxx ,...,,( 21= )) and ε is the insensitivity parameter. In

SVM regression, the basic application of nonlinearly separa-ble data is to map the original dimension of the input data (input space) into higher dimensional space (feature space) by using mapping functions. The mapping function

MNxx ℜ→ℜ⊂:)(φ , where N<<M, is performed by de-

Table 2. Summary of the QSAR/QSPR models.

Method Linear/

Non-linear Classification/

Regression task Advantage Disadvantage

k-NN Linear Classification Simple Unstable and unreliable

MLR Linear Regression Simple Limitation for data with huge numbers of

features

PLS Linear Both Performs well on data with huge numbers of features Linear model

ANN Non-linear Both Performs well on nonlinear data Black-box method

SVM Non-linear Both Most powerful method for both classification and re-

gression Black-box method

DT Non-linear Classification Highly interpretable Requires a large number of training in-

stances

RF Non-linear Both More confident estimate Computationally intensive

Explanation of abbreviations: multiple linear regressions (MLR), partial least square (PLS), k-nearest neighbor (k-NN), artificial neural network (ANN), support vector machine

(SVM), decision tree (DT), and random forests (RF).


fining the inner product between each pair of data points, i.e., two compounds, in the data set of the feature space through the kernel function ),( ji xxK . The overview of

SVM and its kernel function is shown in (Fig. 3D). The ker-nel function ),( ji xxK can be expressed as a similarity

measurement between the training data set, which is defined as:

∑=

==N

jij

Tij

Tiji xxxxxxK

1,

)()()()(),( φφφφ (7)

The most popular used kernels include the linear kernel Φ(xi )

T Φ(xj ) the polynomial kernel (1+ Φ(xi )T Φ(xj ))

d , where d

= 2, 3, and 4 (it should be noted that d = 1 for a linear ker-nel); and the radial basis function (RBF) kernel

))(exp( ji xx −−γ , where γ is greater than 0.

Although the ANN and SVM methods have achieved outstanding performance, these approaches make interpreta-tion of the contained system difficult, which is why they are called black-box methods. DT, also called tree induction, was proposed to mitigate this problem by using a set of esti-mated rules. The decision tree has an efficient built-in fea-ture importance estimator. The C4.5 algorithm is the general-ly used approach for several classification tasks [147, 159]. The construction of the DT model requires the following: 1) all samples in the internal set belong to a single class; 2) the tree depth is close to maximum; and 3) the number of classes in the terminal node is less than the minimum number of classes of the parent nodes. In general, the root node is se-lected from a variable with the highest information gain, whereas the other node or internal node provides the second highest information gain, etc. The information gain of varia-ble v (Gainv ) is calculated as follows:

)(||

||)(log)( 2

1v

Vv

vj

N

jjv DI

DD

CpCpGain ∑∑∈=

−−= (8)

where Gainv is the information gain of feature v on the re-

maining data DDv ⊂ , and p(Cj ) is the probability of the

relative frequency of class j ( jC )[147, 160]. The decision

tree can perform well if enough internal sets are available. The structure of DT with three nodes used to classify a com-pound into either active or inactive class is shown in (Fig. 3E). The logistic model tree was proposed to alleviate this problem and can be applied to classification and regression problems [148, 161].

The RF method is an effective and efficient prediction method based on an ensemble model for solving the classifi-cation and regression problems. Breiman first proposed this ensemble method as belonging to a machine learning tech-nique [149]. This method improved the predictive perfor-mance of classification and regression trees [162] (CART) by growing many weak CART trees. Every tree is construct-ed by using a fixed number of randomly selected features for tree splitting and is based on a bootstrap sample of the whole

internal set. In particular, there are two measurements to select an informative variable: the mean decrease of the Gini index (MDG) and the prediction accuracy (MDA). The MDG has been commonly utilized to estimate feature im-portance because the MDG is suggested to be more robust than the mean decrease of accuracy [163]. The MDG can be defined as follows:

)|(1)( 2 vCpvMDGI jj

∑−= (9)

where )|( vCp j denotes the estimated class probabilities for

feature v in the current decision tree. The feature with the largest value of MDG is the most important feature because it contributes most to prediction performance. This ensemble approach improved predictive performances of CART by selecting from the prediction results of many decision trees [164]. (Fig. 3F) shows the top 13 molecular descriptors ranked by MDG and MDA.

Validating the QSAR/QSPR model: After constructing the QSAR/QSPR model, the internal validation of the pro-posed model is crucial for assessing the reliability of the models and their ability to accurately predict biological ac-tivities or chemical properties. In the classification task, four measurements were generally used to evaluate the prediction performance of the proposed QSAR/QSPR model using cross-validation (CV), namely accuracy (ACC), sensitivity (SEN), specificity (SPEC), and Matthews correlation coeffi-cient (MCC), which are defined in the following equation:

100)(

×+++

+=

FNFPTNTPTNTPAccuracy

(10)

100)(

×+

=FNTP

TPySensitivit

(11)

100)(

×+

=FPTN

TNySpecificit

(12)

))()()(( FNTNFPTNFNTPFPTPFNFPTNTPMCC

++++

×−×=

(13)

where TP, TN, FP and FN represents the numbers of true positive, true negative, false positive and false negative, re-spectively [165]. As for the regression task, various statisti-cal parameters are used for evaluating the robustness of QSAR/QSPR models. Often, the criteria of goodness-of-fit (R2 assessed on the whole internal set) and goodness-of-

prediction ( 2predR assessed by various validation procedures)

are characterized by the coefficient of determination R2 or 2predR and root mean square error RMSE [125]. The R2 and

RMSE are defined as:

2

22

2 ))~~()(

)~~()(

⎥⎥

⎦

⎤

⎢⎢

⎣

⎡

−−

−−=

∑∑∑∑

yyyy

yyyyR

ii

ii

(14)

n

yyRMSE

n

i∑

=

−= 1

2)~( (15)


where y , y~ , y and y~ are the values of actual, predicted,

average value of actual and average value of predicted activi-ties, respectively, while n is the number of compounds. Alt-

hough, high 2predR values are frequently used as one of the

criterion for selecting robust QSAR/QSPR model, it may not afford the most reliable model. Thus, the whole dataset must be divided as internal and external sets, as mentioned above. Tropsha suggested that acceptable QSAR/QSPR models should satisfy the following conditions [166-168]:

6.02 >predR (16)

5.02 >extR (17)

15.1,85.0 ' ≤≤ kk (18)

1.0/)( 220

2 <− predpred RRR

(19) 11.0/)( 22

0'2 <− predpred RRR

where 2predR and 2

extR are the correlation coefficient between

the predicted and actual activities of compounds as assessed by several validation and external sets, respectively, k and

k ′ are the regression coefficients obtained from ykyr ~0 =

and yky r ′=0~ , respectively, and 20R and

20'R are calculated

as follows:

∑∑

−

−−=

2

20

20

)~~(

)~(1

yyyy

Ri

rii

(20)

∑∑

−

−−=

2

2020

'

)(

)~(1

yyyy

Ri

rii

(21)

where iy~ is the ith compound from the external set. The

basic form of cross-validation is known as the k-fold cross-validation (k-fold CV). For example, for a 10-fold CV exper-iment, the data are first partitioned into 10 equally (or nearly equally) sized segments or folds, then 9 segments are used for training and the remaining segment is used for validation. Finally, the results are then averaged across the 10 experi-ments. Leave-one-out cross-validation (LOO-CV) is a spe-cial case of k-fold CV where k equals the number of instanc-es in the data. The LOO-CV method is a widely used ap-proach when the available data are rare, especially in bioin-formatics where only a few data points are available [169].

There are several statistical techniques to evaluate the predictive ability of a QSAR/QSPR model including external validation, Y-randomization test, domain of applicability and the William plot [124]. External validation is commonly used to evaluate the external predictivity of a QSAR/QSPR model by leaving out a subset of data at the onset of the ex-periment while the remaining internal set is used for evaluat-ing optimal parameters of learning algorithms. The main

goal of this validation is to provide a reliable performance evaluation and simulating the general performance of the model on compounds with unknown activity/property. Prac-tically, in order to perform a QSAR/QSPR model on new screening compounds, its domain of applicability should be calculated [170]. The domain of applicability can be charac-terized using the Euclidean distance (Eq. 1) amongst all pos-sible pairs between internal and external sets [167, 171]. Prediction of the new screening compound is considered acceptable when the distance of the new compound to its nearest neighbor in the internal set was lower than that of the predefined applicability domain. The second approach in defining the domain of applicability of a QSAR/QSPR mod-el can be evaluated from the leverage value of each com-pound [172]. In cases when compounds have larger leverage, it means that the prediction may be a substantial extrapola-tion of the QSAR/QSPR model and may not be reliable. In summary, the new screening compound falling into its do-main of applicability may be considered reliable. Finally, the Y-randomization test or Y-scrambling is the most commonly used parameter for regression task in assessing models for chance correlation [167]. This test is performed by construct-ing a model in which the Y variable (i.e. activity of com-pound) is randomly shuffled but the X variable (i.e. molecu-lar descriptor) is kept constant. The model is then retrained to generate a new scrambled model. If the original model has no chance correlation, the result of scrambled models could

be expected to provide low 2R and 2predR values.

Pharmacophore and CoMFA

A pharmacophore is defined as a two- or three-dimensional arrangement of the chemical features of com-pounds that are required for optimal interaction with the pro-tein target [173] and contribute to biological responses [174]. The pharmacophore concept was first introduced in the 1900s by Ehrlich. The concept proposed by Ehrlich stated that a pharmacophore is not the same as a functional groups of molecules; rather, it is a molecular scaffold that carries essential features responsible for the compounds’ bioactivity [175]. Pharmacophores can be grouped into two classes on the basis of the method that is used to obtain them [173]. The first class is structure-based pharmacophores, which is based on probing the possible interaction points between the ligand and the target [173]. The second class is ligand-based phar-macophores, based purely on the structure and binding data of the ligand to the target without consideration of the three-dimensional structure of the target proteins for which many active molecules are superimposed to extract the common features that are crucial for bioactivity [176, 177]. Thus, pharmacophore modeling intuitively produces results per-taining to the interactions between chemical ligands and tar-get proteins in three dimensions, and the resulting features can be derived by computational algorithms that extract in-formation from large quantities of data [178]. Conceptually, common structural features of bioactive and bioinactive chemical compounds of protein targets are identified by con-sidering many features of the compounds, e.g., the spatial arrangement of features, physicochemical properties, steric characteristics, bonding capabilities and quantum chemical properties of chemical compounds, including hydrogen-bond


Table 3. List of softwares related to pharmacophore modeling.

Name Description Software Format

Availability URL

Align-it (Pharao)

Pharmacophore alignment Standalone Free http://silicos-it.be

Catalyst Pharmacophore modeling Standalone Commercial http://accelrys.com/products/discovery-studio/pharmacophore.html

CATSlight2 Topological pharmacophore descriptor for scaffold hop-

ping and target identifica-

tion

Web service Free http://modlab-cadd.ethz.ch/software/catslight2/

FLAP Fingerprints using pharma-cophoric features

Standalone Commercial http://www.moldiscovery.com/software/flap/

LigandScout Pharmacophore modeling Standalone Commercial http://www.inteligand.com/ligandscout/

MOE Pharmacophore modeling Standalone Commercial http://www.chemcomp.com/MOE-Pharmacophore_Discovery.htm

MolSign Pharmacophore modeling Standalone Commercial http://www.vlifesciences.com/products/Functional_products/Molsign.php

PharmaGist Pharmacophore detection Standalone/

Web service

Free http://bioinfo3d.cs.tau.ac.il/pharma/

Pharmer Pharmacophore search Standalone Free http://smoothdock.ccbb.pitt.edu/pharmer/

PharmMapper Drug target identification Web service Free http://59.78.96.61/pharmmapper

Phase Pharmacophore modeling Standalone Commercial http://www.schrodinger.com/Phase/

Quasi Pharmacophore modeling Standalone Commercial http://www.denovopharma.com/page2.asp?PageID=485

UNITY Pharmacophore modeling Standalone Commercial http://www.certara.com/products/molmod/sybyl-x/simpharm/

ZincPharmer Pharmacophore search for

purchasable compounds

from ZINC database

Web service Free http://zincpharmer.csb.pitt.edu

donors, hydrogen-bond acceptors, charged groups, hydro-phobic interactions [179] and the three dimensional ar-rangement of the target protein. The pharmacophore models were built and validated with different compound series to determine whether the active compounds fit the pharmaco-phore. Finally, a reliable and robust pharmacophore model was obtained by returning a large series of compounds with preferable binding modes to the target [180]. Thus, the pharmacophore modeling merges information from struc-ture-activity with the active sites of protein targets [181]. The capability of binding to the target protein of such chemi-cal compounds indicates that some portions of the com-pounds are important and are responsible for favorable inter-actions with the target [182]. By using the insights derived from the pharmacophore model, the important functional groups that are essential for interacting favorably with the target and that contribute to bioactivity can be identified. Pharmacophore features have been widely used for virtual screening, de novo design and lead optimization [183].

CoMFA (Comparative Molecular Field Analysis), also known as the 3D-QSAR method, can be used to identify pharmacophores by correlating ligand 3D structures with their binding activities. The structures of ligands are super-imposed to identify common features that are responsible for their biological activities without requiring 3D structures of

target proteins. Typically, parameters such as steric energies, electrostatic interactions and the location of an atom at lattice intersections, together with bioactivities, are used to build these predictive models [184]. Because CoMFA will usually extract a large number of parameters, the partial least square, a commonly used liner modeling method, simultaneously projects the extracted parameters from CoMFA with bioac-tivities into latent variables to correlate multiple parameters with bioactivities. The extent of the parameters’ influence on bioactivities is indicated by regression coefficients [185].

DISCO (DIStanceCOmparisons) is the first automated pharmacophore modeling method that can systemically ana-lyze and match the conformation of diverse molecules [186] by using the Bron-Kerbosh clique-detection algorithm [187]. The superposition rule of bioactive conformation is used to identify common pharmacophoric features. However, the identification is based on the distance points of intramolecu-lar interactions of conformations within chemical com-pounds without consideration of the three dimensional spa-tial arrangement of the target [187]. DISCO has been suc-cessfully utilized to identify pharmacophores of dopaminer-gic agonists [187], ligands of nucleoside transporters hCNT1 [188], antihypertensive drugs [189], cAMP PDE III inhibi-tors [190], neuronal nicotinic receptor agonists [191], inhibi-tors of vitamin D hydroxylases [192] and cGMP phos-phodiesterase inhibitors [193].


Currently, many automated pharmacophore generator software programs have been developed for application in drug discovery and development [183], such as GASP [194], HipHop [195], HypoGen [196], MOE [197], PHASE [198] and GALAHAD [199]. LigandScout is an integrated plat-form for 3D virtual screening and pharmacophore modeling. It considers ligand-protein interactions, including hydrogen bonding, π- π stacking, Van der Waals, charge transfer, elec-trostatic and hydrophobic interactions [200].

Molecular Docking and Molecular Dynamics Simulations

There has been an explosive growth in the available structural data for proteins by X-ray crystallographic and NMR spectroscopic studies and derived from large amounts of genomic and proteomic data by theoretical modeling. For this reason, discovering new drug targets relies on accurate modeling of these data in rational drug design because in-formation from both protein structures and their ligand-binding sites can be exploited. In this case, two widely used methods, molecular docking and molecular dynamics simu-lation, play a major role in these approaches and are usually combined to investigate interactions of small molecules with the protein target at the atomic level [201].

Molecular docking is a term used for the computational scheme that attempts to search for the possible binding modes of a ligand with its receptor [202]. Docking algo-rithms have been developed to generate a comprehensive conformational set of protein-ligand complexes, which sub-sequently scores them according to their stability. Several factors influence the process of a ligand binding to its pro-tein, including thermodynamic and solvation contributions and the charge distributions of the protein and ligand mole-

cules [203]. In any docking scheme, two conflicting re-quirements should be balanced: the desire for an accurate procedure and the desire to keep the computational demands at a reasonable level. Thus, the ideal docking protocol should explore all available degrees of freedom for a particular sys-tem to reach the global minimum in the free energy of bind-ing within an amount of time comparable to other laboratory working computations [204]. In contrast to molecular dock-ing, molecular dynamics (MD) simulations, which represent one of the most versatile computational techniques for study-ing the dynamics of biomolecules, are more computationally expensive and sophisticated. These simulations generate a set of conformations of a biomolecule by iteratively integrat-ing (numerically) the equations of motion for a specific po-tential function with certain initial and boundary conditions [205]. A structural ensemble generated from an MD simula-tion can be used to explore the conformational space of bio-molecule, to calculate thermodynamic quantities and to esti-mate the free energy of biological processes [206]. In the prediction of the strength of non-bonded interactions, the MD technique has been widely used in free energy binding calculations, which cover a broad range of accuracies and computational requirements. Other computationally expen-sive but highly accurate methods include the free energy perturbation (FEP) and thermodynamic integration (TI) methods, whereas the linear interaction energy (LIE) and molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) methods, which increase computational speed at the expense of accuracy, have been developed more re-cently [207].

Once the structure of a protein target is known, the pro-cess of rational drug design follows a well-established proto-col, as shown in (Fig. 4). As mentioned previously, molecu-

Fig. (4). Schematic representation of the protocol combining molecular docking and molecular dynamics simulations that is applied during rational drug design such that the structure of the protein target can be experimentally or theoretically obtained. (Hits = compounds that can bind to a target, Leads = hit compounds with more preferable potency, MD = molecular dynamics).


lar docking techniques can be applied during the high throughput virtual screening stage to scan a large compound library and identify small molecules that are more likely to bind to the protein target. This initial screening employs of inexpensive and fast docking algorithms to evaluate the binding affinities. Subsequently, the selected compounds will be subjected to additional docking experiments using more accurate scoring functions. Once a few hit compounds are identified, MD can then be used to refine such docked complexes, which can account for effects of induced fit and explicit solvation and can test the stability of the complex over (simulated) time. When the well-equilibrated MD en-semble has been generated, it can be used for the calculation of more accurate binding free energies, e.g., LIE and MM/PBSA, which are expected to provide much better scor-ing than the simple algorithms used during the initial dock-ing step. Therefore, the two techniques in the protocol, in which molecular docking is used for the fast screening of a large library of compounds and MD simulations, are sequen-tially applied to optimize the structure of the final complex-es. Finally, accurate binding free energies are then calculat-ed, which makes for a rational approach that helps to im-prove the drug discovery process [208, 209].

The use of combined docking and MD methods has been broadly applied for the identification of new therapeutic agents from compounds of natural origin and the optimiza-tion of new lead candidates derived from natural compounds. Recent examples using molecular docking-based virtual screening for the discovery of potent inhibitors from natural product databases have been extensively reviewed [210]. The identified bioactive compounds target many biological processes, including enzyme-substrate interactions, receptor-ligand interactions and DNA interactions [210]. The most promising targets for natural compounds are protein kinases, and others include DNA methyltransferases [211], aldose reductase [212], viral enzymes [213], and beta amyloid (Aβ) peptides [214]. Several studies based on biophysical and docking experiments clearly demonstrate that various flavo-noids including myricetin, quercetin, caffeic acid, daidzein, delphinidin, and procyanidin can bind directly to several protein kinases such as Akt/protein kinase B (Akt/PKB), Fyn, Janus kinase (JAK) 1, mitogen-activated protein kinase

kinase (MEK) 1, phosphoinositide 3-kinase (PI3K), mitogen-activated protein kinase kinase (MKK) 4, and Raf1. Notably, all of these kinases control multiple cell signaling pathways in oncogenesis [215]. Recently, several compounds from the traditional Chinese medicine (TCM) database [216] were successfully identified as potent inhibitors of human epider-mal growth factor receptor (HER) 1, and 2 tyrosine kinases that have been known to be associated with several types of cancer have been identified by combining docking, 3D-QSAR and MD simulations [217-219].

As mentioned, MD simulations combined with the MM/PBSA method can be widely used to obtain detailed in-formation on the binding efficacy of drug-target interactions. This approach has also been applied to investigate the inhibi-tory efficiency of natural compounds to several protein targets, including cyclin dependent kinase (CDK) 6 [220], HER2 ki-nase [221], and Aβ peptides [222, 223]. The theoretical results are in good agreement with experimental results, suggesting the efficiency of the method to predict accurate binding affini-ties that are beneficial for the validation of drug-target com-plexes. In addition to the free energy calculations, MD can provide valuable information by giving dynamical information of protein structures. MD has been applied to study the dy-namic events of targeting Aβ peptide aggregation by morin, one of the most effective anti-aggregation flavonoids [214]. Lemkul et al. [224] conducted long MD simulations to demonstrate that morin can destabilize the Aβ42 protofibrils by blocking the attachment of an incoming peptide onto the growing end of an Aβ42 fibril and can disrupt the crucial inter-peptide salt bridges, which are an important contribution in the stability of the Aβ42 protofibrils. Extended work from the same group also showed that morin can inhibit the early stages of Aβ peptide aggregation by affecting the tertiary and quater-nary structure of premature Aβ40 and Aβ42 monomeric and dimeric states that give rise to different structures, which pre-sumably result in o�-pathway aggregates that may have re-duced toxicity compared to untreated peptides [225]. Conse-quently, the examples given here demonstrate how one can utilize MD as a tool to understand the mechanism by which potent natural compounds act on the structure of protein tar-gets. For example, (Fig. 5). shows the binding pose of natural product curcumin I at the active site of HER2 kinase.

Fig. (5). Binding pose of natural compound (curcumin I, Diferuloylmethane) at the active site of HER2 kinase previously investigated by Yim-Im et al. [221]. The compound and amino acid residues are represented in sticks with larger and smaller sizes, respectively (carbon, grey; nitrogen, blue; oxygen, red). Hydrogen bond and hydrophobic interactions are indicated in green and pink dashed lines, respectively.


CONCLUSION

The prestige of traditional medicine has been recognized by its effectiveness in curing diseases and its ability to im-prove the quality of life from antiquity [226, 227]. In the past, the use of natural products as therapeutic agents was restricted in developing countries and rural regions where medical facilities were inaccessible and unaffordable [15]. Recently, the use of natural products has become more popu-lar and acceptable worldwide, especially in developed coun-tries where advanced modern medicine has been developed [228]. People pay more attention to traditional medicine and natural products because of their concern about the adverse side effects of synthetic drugs [229-231]. Furthermore, most of the population in the globalization era suffers from life-style-related, stress-related and aging diseases. These chronic diseases are related to the changing lifestyle in which society is more concerned about the way individuals eat and live [230]. The return of herbal medicines can be observed from the parallel use of complementary and alternative medicines with modern medicine to improve treatment outcomes [232], as well as via the trends of using natural products for the prevention and promotion of good health, i.e., dietary sup-plements [233, 234].

Many countries have established unique herbal medicine systems with regards to their cultural history, ecology and medical anthropology [15]. Most of the traditional medicine systems often prescribe combinations of herbal mixtures, and their therapeutic effects are based on synergistic or antago-nistic effects among each other [15]. Although it is widely believed that herbal medicines are safe, serious undesired side effects have been reported [235]. Hence, in an attempt to contribute to the health of the world’s population, the World Health Organization (WHO) has encouraged a prime focus on herbal medicines to standardize regulations across countries and promote their safety and efficacy [228, 235].

Natural products are major sources of inspiration for the discovery of new drugs and are of great value to the field of drug development [109]. With respect to the power of Mother Nature, all organisms select chemicals for synthesis, consump-tion and utilization based on evolutionary advantage [95]. Di-verse types of biologically relevant privileged structures are provided by natural products, especially plants [104]. It should be noted that some of these scaffolds share common core structures with different substituent patterns, which give rise to their different bioactivities within the same organism or across species [84]. In other words, it can be stated that scaf-folds of natural products are evolutionary-chosen [84]. Hence, scaffolds from natural products serve as structural starting points to explore the biologically relevant chemical space [86], and the modification to these privileged structures is required for preferable therapeutic properties [97].

Computational approaches are fundamental for drug dis-covery and development, with no exception for naturally derived drugs. Databases have been developed to aid drug design and discovery and participate in the identification of hits and druggable targets. Computational approaches (i.e., molecular docking, QSAR, QSPR, chemoinformatics and data mining) can provide insights into many aspects of target proteins, naturally occurring privileged structures, and pro-tein-ligand interactions, which are essential for the discovery

and development of novel lead compounds enriched with bioactivities. Because most natural products are not designed for human use, the transformation of naturally occurring bioactive compounds into human drugs requires modifica-tion and evaluation by multidisciplinary teams of experts. Computational tools provide understanding on structure-activity or structure-property relationships, which are useful guidelines for the design and synthesis by organic/medicinal chemists to obtain compounds with improved potency, selec-tivity, and drug-like properties and reduced undesirable side effects. Furthermore, computational chemogenomics (related concepts include proteochemometric modeling, polypharma-cology, systems pharmacology) facilitates the seamless inte-gration of bioinformatics and cheminformatics by allowing the interaction of several proteins and several ligands to be investigated. Such approach has great potential for drug re-positioning, target identification, ligand profiling and recep-tor deorphanization.

To place this field of research into greater perspective, populations worldwide face chronic and multifactorial dis-eases relating to changing lifestyles and aging conditions. Behavior-related diseases are becoming the foremost health issue that must be addressed. Most chronic diseases arise from unhealthy lifestyles and continual exposure to harmful chemicals. This situation ought to stimulate people to have greater concern about how they spend their life. Healthy life-styles and eco-friendly products are becoming fashionable for new generations. In addition, the polypharmacology-based principle of traditional medicine is expected to provide favorable treatment outcomes against multifactorial diseases. Therefore, traditional medicine systems and natural products are returning as an alternative method of treatment, with em-phasis on their safety because they are naturally derived. Furthermore, significant attention has been given to natural products because of their influence on human well-being, as they yield beneficial sources of bioactive ingredients for cosmeceuticals and nutraceuticals. In summary, natural products are of great benefit to mankind, and extensive re-search on these natural treasures would provide substantial impact for the betterment of society.

CONFLICT OF INTEREST

The author(s) confirm that this article content has no con-flict of interest.

ACKNOWLEDGEMENTS

This research project is supported by the annual budget grant of Mahidol University (B.E. 2556-2558), Mahidol University Talent Management Program to A.W. as well as the Office of the Higher Education Commission and Mahidol University under the National Research Universities Initia-tive.

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Received: December 05, 2014 Revised: February 19, 2015 Accepted: February 20, 2015

Computer-Aided Drug Design of Bioactive Natural Products

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