Virtual Screening Strategies in Drug Discovery: A Critical Review

Transcription

1 Current Medicinal Chemistry, 2013, 20,????-???? 1 Virtual Screening Strategies in Drug Discovery: A Critical Review A. Lavecchia* and C. Di Giovanni Department of Pharmacy, Drug Discovery Laboratory, University of aples Federico II, via D. Montesano 49, I apoli, 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 structurebased 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 strategies 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. ITRDUCTI 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) deficiencies. It was therefore necessary to develop alternative strategies that could help to choose an appropriate set of compounds, 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 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 publications 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 aples Federico II, via D. Montesano 49, I apoli, Italy; Tel: ; Fax: ; 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 alternatives 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 confidence homology models are available. VS is usually described 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 targets. 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 evaluated 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 compounds can be obtained either from a multitude of external sources such as compound libraries from commercial vendors 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 experimental evaluation [3]. LBVS methods include approaches such as similarity and substructure searching, quantitative structure-activity relationships (QSAR), and pharmacophoreand three-dimensional shape matching [4]. SBVS, on the other hand, utilizes the three-dimensional (3D) structure of /13 $ Bentham Science Publishers

2 2 Current Medicinal Chemistry, 2013, Vol. 20, o. 1 Lavecchia and Di Giovanni the biological target (determined either experimentally through X-ray crystallography or MR or computationally through homology modeling) to dock the candidate molecules and rank them based on their predicted binding affinity or complementarity to the binding site. a link with the disease under scrutiny such that pharmacological intervention would be expected to cure the disease or ameliorate its symptoms. This first step consists of identifying potential targets and validating them as actual targets. Identification of potential targets requires exploration of Biological Space (Fig. 2) through the sequencing of the human genome, with their reliance on high throughput sequencing technologies and computing algorithms to handle and analyze the large volumes of data being generated. Having identified 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 [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. (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 Journal a Journal o 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 ature 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 methodology and to reveal the expectations for realistic goals. SMALL MLECULE DATABASE MAAGEMET The first requirement in conventional drug discovery is the identification of a valid target, i.e. a molecule which has 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 efficacious in treating disease. VS has the unique opportunity to expand 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 ~10 6 compounds, a practically infinitesimal fraction of the entire space. Fortunately, however, only a small portion of that space can be expected to comprise 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-

3 Virtual Screening Strategies in Drug Discovery: A Critical Review Current Medicinal Chemistry, 2013, Vol. 20, o. 1 3 ficity (Medicinal Chemistry Space). It is assumed that the Chemical Space represented by traditional screening collections is inadequate to successfully tackle promising unvalidated 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 productlike combinatorial libraries. otably, 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 Properties Typically Used for Lead-likeness Criteria [11] Lead-likeness Molecular Weight (MW) Lipophilicity (CLogP) -4/4.2 H-bond donor (sum of H and H) H-bond acceptor (sum of and ) 5 9 Polar Surface Area (PSA) 170 Å 2 umber of rotatable bonds CAC-2 membrane permeability Solubility in water (logs) -5/0.5 thers Absence of both toxic and reactive fragments The concept of drug-likeness has been introduced to determine the characteristics necessary for a drug to be successful. So, over time, more stringent rules and guidelines for lead- and drug-like features have been applied for compounds in a screening library. (Table 2) lists the lead-like guidelines suggested by Hann and prea [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-druglike" 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 recently revisited using pharmacokinetic data in rats [13]. Many related rules have been subsequently modified and proposed as the "Rule-of-Three" [14], which defines fragment 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 ame URL Vitas-M Allium Library 3 TimTec Fragment-Based Library 3 and 5 ChemBridge Fragment Library 3 Lifechemicals General Fragments Library 3 ASIEX's BioFragments 3 Enamine Fragment Library 3 Keyorganics BIET Fragment Library 3 Maybridge Ro3 Library 3 Maybridge Screening Collection 5 TAVA Fragment Library 3 Prestwick Fragment Library 3 ChemDiv Fragment Based Library 3

4 4 Current Medicinal Chemistry, 2013, Vol. 20, o. 1 Lavecchia and Di Giovanni bazide), which are associated with metabolism-mediated toxicity. Alternatively, groups such as aldehydes and epoxides may be considered inappropriately electrophilic, whereas others such as thiols are redox active. Unsuitable leads may include crown ethers, disulfide, and several natural scaffolds such as quinones, polyenes, and cycloheximidine derivatives. Further problems concern some compounds that may be autofluorescent and others that may aggregate [24] at certain concentrations and produce false positives 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 protein serum) to support their detection experimentally [25]. In short, these bad molecules encompass chemically reactive, assay-interfering compounds and are often referred to as PAIS (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]. Frequent 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]. ther unsuitable groups are detected by the ALARM MR 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 unrealistic conformations of molecules. Some conformational sampling methods do not employ energy minimization to refine and properly rank the resulting geometries, thus providing high-energy conformations. If such geometries are not eliminated, 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. f course, answering this question implies that the concept of molecular diversity [34] has previously 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 factor to the success of the screening process. It should be ascertained 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 nonredundant scaffolds among all available libraries in order to customize its screening database. As most of these collections are quite dynamic entities, it is also advisable to upgrade their stock in order to reflect recent changes in individual 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]. ZIC [40] is a free database of commercially available compounds that in its current version contains over 13 million purchasable compounds 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 CBI public informatics backbone for the IH 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 million records of chemical substances, 25 million unique compound structures and bioactivity data from more than 1,600 assays. ther 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 ational Cancer Institute (CI) pen 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 CI database contains ~15,000 additional structure records not present in the CI pen Database. CI also offers four sets of compounds with different characteristics: i) the CI atural Products Set II, that contain 120 natural products derived from plants and microbes; ii) the CI Mechanistic 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 CI Diversity Set, which is a restricted collection of 2,000 compounds with some lead-like properties such as 5 or fewer rotatable bonds, 1 or less chiral centers and pharmacologically desirable features (i.e., they are not electrophilic, unstable, organometallic, polycyclic aromatic hydrocarbons, etc.). The French national chemical library of the Chimiothèque ationale has 48,370 synthetic and natural compounds available thanks to a material transfer agreement and a small financial contribution. The St. Louis Compound Collection contains 60 small molecules (a number which is growing) useful for screening campaigns in the field of

5 Virtual Screening Strategies in Drug Discovery: A Critical Review Current Medicinal Chemistry, 2013, Vol. 20, o. 1 5 Table 4. Small Molecule Databases and Compound Collections Available from Vendors or Institutions Database Type o. Cpds Website ZIC [40] Public 13 million ChemDB [41] Public 5 million emolecules Commercial 7 million ChemSpider Public 26 million Pubchem Public 30 million ChemBank [42] Public 1,2 million DrugBank [43, 44] Public 4,800 drugs; 2,500 targets CI pen Database Public 265,000 Chimiothèque ationale Commercial 48,370 Drug Discovery Center Collection Commercial 340,000 ChEMBL [45] Public 1 million WMBAT [46] Commercial 263,000 ChemBridge Commercial 700,000 Specs Commercial 240,000 CoCoCo [47] Public 7 million Asinex Commercial 550,000 Enamine Commercial 1,7 million Maybridge Commercial 56,000 ChemDiv Commercial 1,5 million ACD Commercial 3,9 million MDDR Commercial 150,000 infectious diseases available from CI. The Drug Discovery Center Collection of the University of Cincinnati (UC Compound 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 (WMBAT) 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]. ow, more than 60% of new compounds entering the Chemical Abstract Service (CAS) registry come from patents. STRUCTURE-BASED AD LIGAD-BASED VIR- TUAL SCREEIG APPRACHES The application of VS for hit and lead identification follows a typical sequence of processes. Depending on the information obtainable about the target and/or existing ligands at the beginning of the screening campaign, VS can be traditionally 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 6 Current Medicinal Chemistry, 2013, Vol. 20, o. 1 Lavecchia and Di Giovanni Table 5. Targeted Small Molecules Databases from Commercial Vendors Company Library ame Link Address Asinex Antibacterials SPECS Kinase-targeted Library GPCR Ligands Kinase Modulators Timtec ChemBridge ChemDiv InterBioScreen MayBridge Key rganics Life Chemicals Protease Inhibitors Potassium Channels Modulators uclear Receptors Ligands Kinase-Biased Sets GPCR Library Channel-Biased Sets GPCRs Kinases IBS High-Hit Databases Analgesics Antibacterials Antidiabetics Cancerostatics Cns regulators Bionet Antimalarial Agents Active Compounds for Cancer Research Active Compounds for CS Research GPCR Library Kinase Library Anticancer Library Table 6. Example of Commonly Used Docking Softwares Software Free for Academia Website AUTDCK [49] Yes DCK [50] Yes FlexX [51] o GLIDE [52] o GLD [53] o EADock [54]

7 Virtual Screening Strategies in Drug Discovery: A Critical Review Current Medicinal Chemistry, 2013, Vol. 20, o. 1 7 (Table 6) contd. Software Free for Academia Website Surflex [55] o ICM [56] o LigandFit [57] o ehits [58] o SLIDE [59] Yes on demand RSETTA_DCK [60] Yes on demand Virtual Docker [61] o Ligand-Receptor Docking [62] o FRED [63] Yes on demand ZDCK [64] Yes pharmacophores or descriptor values. Depending on the available information, both methods can be used individually or in combination. one of these proposed approaches can be a priori considered superior to the other because, as suggested 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 ligandbinding 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 molecular 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 compounds. 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. Solvation [73, 74] and entropy [75] contributions can also be considered. 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 complexes [78]. The knowledge-based scoring functions [79, 80] are exclusively derived from statistical analyses of atompairs frequencies in protein-ligand complexes with known 3D structures. ver 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. evertheless, 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 affinities [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 calculations [84], target-specific scoring functions [85] and consensus scoring by combination of multiple scoring functions [86, 87]. n 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 parameters 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 geometry 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 structural 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 algorithms are directed towards properly considering target flexibility. 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 motions. Several docking programs including AUTDCK4 [49], DCK [50], GLD [53], EADock [54], IFREDA [56], FlexE [90], or GLIDE induced Fit [91] (Table 7) allow rotation around torsional degrees of freedom of selected side

8 8 Current Medicinal Chemistry, 2013, Vol. 20, o. 1 Lavecchia and Di Giovanni Table 7. Docking Programs That Include Protein Flexibility Program and Ref. Ligand Flexibility Protein Flexibility Scoring function AUTDCK4 [49] Evolutionary algorithm Flexible side chain Force field DCK [50] Incremental build Protein side chain and flexibility Force field or contact score GLD [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 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 development and their applications are particularly interesting for VS experiments. ne 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 combines the advantages of docking algorithms with dynamic structural information provided by MD simulations, explicitly 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 software packages (including AMBER [94], AMD [95], GRMACS [96] and AUTDCK 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. nce 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 pharmacophore-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 detected compounds will pass all the stages of drug development. 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 inaccurate conformational sampling and molecular overlay, ambiguities in pharmacophore pattern (mainly due to uncertainty related to the protonation/tautomeric status of compounds), 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 relationships (QSAR). The aim of QSAR is to determine the relationship between structural/physicochemical properties of active compounds to their biological activity [ ]. Knowledge of the activity levels of the compounds, such as binding affinity (K D ) or inhibitory concentration (IC 50 ), is requisite for QSAR methods. Molecular structures are normally represented by specific sets of structural and physicochemical properties (molecular descriptors) of the molecules considered most relevant to their binding activities. The quality of QSAR models is affected by compatibility of concepts, 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]. ne 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 [ ]. The analysis can be done by topological indices, molecular fingerprints, fragment or substructure based descriptors. Some examples of the common molecular fingerprints based on 2D or 3D descriptors are represented by MLPRIT 2D [109], Property Descriptor value Range-derived FingerPrint (PDR- FP) [110], Rapid verlay of Chemical Structures (RCS) program [111] and shape fingerprints [112]. A new application of the similarity analysis is based on the use of mapping methods. Mapping algorithms originated with the introduction of dynamic mapping of consensus positions (DMC) determine 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 performance 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

9 Virtual Screening Strategies in Drug Discovery: A Critical Review Current Medicinal Chemistry, 2013, Vol. 20, o. 1 9 quantitative structure-activity relationships. Different techniques are proposed and each method has its own advantages and disadvantages. Among these methods, regression and classification methods such as Multiple Linear Regression, earest eighbors, aïve Bayesian Classification, Support Vector Machines, eural etworks 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 capture the properties discriminating active molecules from inactive 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 typically outnumber active compounds), and parameter ranges in covering active and inactive chemical space [117]. Machine learning models capable of screening large compound databases with good yields of actives and low false-hit rates can be developed by using highly diverse training datasets composed of predicted inactive compounds [ ]. CMBIIG LIGAD AD STRUCTURE-BASED APPRACHES In recent years there has been increasing interest in combining 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, MR, comparative modeling) and known active compounds are available and a number of attempts have been made to do so [ ]. In most of these cases, SBVS and LBVS techniques are combined in a sequential or parallel manner. The serial combination 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 compounds of both methods either as a complementary selection (top ranked compounds from each methods) or a consensual selection (compounds selected by both approaches). Unfortunately, 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 approaches involve either preselecting or rescoring the structurebased 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 currently there is no approach which permits distinguishing the good from the bad. I SILIC CHEMGEMICS APPRACHES The Human Genome Project [131] and the birth of accompanying technology has made a breakthrough regarding future medicinal chemistry. Sequences, identified thanks to the Human Genome Project and structure databases in combination 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 biological effect of a large number of small molecules (ligands) on a large number of macromolecular targets (gene products) [ ]. Since experimental measurements of a large number of ligand target interactions are time-consuming and cost intensive, they are often complemented by highthroughput in silico chemogenomic approaches. Typically, these VS methods are divided into ligand-based and targetbased 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 discovery context [137, 138] with the aim of guiding the development of a compound with the desired bioactivity profile [ ]. It should be noted that the applicability of in silico chemogenomics models depends on the quality and completeness 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 usually 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 selectivity needs to be achieved [144]. Furthermore, most scientific studies focused on the presentation of active molecules 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 [ ], PDBBind [148], MAD [149, 150], WMBAT [46], and GLIDA [151] protein ligand interaction matrices are incomplete [142]. Computational methods have, however, been successfully used to fill the gap in experimental ligand-target affinity matrices [152] and to identify new drug target associations [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 protein classes [153]. The principle that similar receptors bind similar ligands [154] is used in LBVS methods that extrapolate 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 ligandbased 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 10 Current Medicinal Chemistry, 2013, Vol. 20, o. 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 research, 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 chemical starting points or tools for the investigation of new targets. 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 insight into the disease-relevant targets and pathways leading to better therapeutic approaches are further benefits of chemogenomics applications [162]. Last but not least in phenotypic screening, the bioprofile of an active compound may help to identify the relevant target(s) [163]. RECET VIRTUAL SCREEIG CASE STUDIES The number of applications of VS has been rapidly mounting in recent years. In this section, we report twentythree 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 (IC 50, EC 50, K i, or K d ) of the VS hits identified, i.e. < 1 μm, between 1-10 μm and between 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 potent 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 design of combinatorial libraries based on a benzoxazole template to discover novel and potent soluble epoxide hydrolase 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. f the 383 final compounds synthetically delivered, 90% were found active in the soluble epoxide hydrolase enzyme assay, thus corroborating the high success rate of the design. Subsequent IC 50 determination confirmed 23 compounds as single digit nanomolar soluble epoxide hydrolase inhibitors. The most active compound 1 (Chart (1) and (Table 8) showed an IC 50 of 3.6 nm. A retrospective analysis showed that docking scores computed by five different scoring functions (FlexX, DCK, GLD, ChemScore, and PMF) resulted less effective at differentiating good binders from poor ones than the hydrogen bond criteria used in the design. These results, once again, reveal the deficiency of scoring functions as they are designed to be rapidly evaluated and applicable across heterogeneous receptors and ligands. Cl H 1 IC 50 = 3.6 nm S 2 IC 50 = 9.4 nm 3 K i = 65.3 nm S H CH 4 K i = 75.3 nm C 7 IC 50 = nm S H CH H H 8 IC 50 = 78 nm Chart (1). Chemical structures of hits with potency < 1 μm. S 5 IC 50 = 44 nm Cl H 3 S H H Cl H H 9 IC 50 = 400 nm H 6 K i 5-HT 2A = 1959 nm K i 5-HT 2B = 56 nm K i 5-HT 2C = 417 nm CF 3 H H F 3 C H CH H 10 IC 50 Cdc25A = 240 nm IC 50 Cdc25B = 100 nm Cl

11 Virtual Screening Strategies in Drug Discovery: A Critical Review Current Medicinal Chemistry, 2013, Vol. 20, o A simple but successful VS protocol published by Caporuscio et al. [165] aimed to discover new aromatase inhibitors as potential drugs in estrogen receptor positive breast cancer therapy. Cytochrome P450 aromatase (CYP19) catalyzes the rate-limiting step in the biosynthesis of estrogens by the aromatization of the A ring of androgen precursors such as androstenedione and testosterone. Many aromatase 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 nitrogen 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 descriptors relevant to receptor-ligand binding such as good protein/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 IC 50 < 100 nm on the CYP19 aromatase. The most active compound 2 (Chart (1) and (Table 8) showed an IC 50 of 9.4nM and favourable ADME properties (predicted log octanol-water partition coefficient (QPlogPo/w ) = 2.94; predicted log aqueous solubility 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 cytotoxic 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 design. 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 Ambinter, Chembridge, Lifechemicals, etc.) was used. So, 105 sildenafil-like compounds were retrieved and docked using a 4D (multi-conformational) VS docking (ICM software package) [56] into the ABCC5 transporter. 4D VS docking is an original computational technique called fumigation capable 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 repulsive 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 K i 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 compounds on 17b-hydroxysteroid dehydrogenase type 2 (17 - HSD1) receptor. This latter, which is responsible for the intracellular AD(P)H-dependent conversion of the weak estrone E1 into the highly potent estrogen E2, was found overexpressed at mra level in breast cancer cells and endometriosis. An inhibition of this enzyme could selectively reduce the local E2-level thus allowing for a novel, targeted approach 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 perating Environment (ME) [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 IC 50 value of 44 nm for the transformation 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 pharmaceutical 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 docking and molecular dynamics (MD) simulations was used to predict the potential 5-HT 2A off-target activity of FDA approved 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-HT 2A, respectively. The induced fit complexes were used as the starting point for further global structure refinement via MD simulation including explicit lipid membrane and water environment by Desmond software [169]. Then, the refined induced fit models were used to virtually screen the ZIC- FDA drug library filtered with drug-like criteria by DCK software [50] and re-scored by MM-GB/SA method. The first 200 top-ranked solutions were subjected to further structural analysis and visual check. f the six top scoring hits chosen for experimental assays, surprisingly, one wellknown kinase inhibitor, sorafenib, compound 6 (Chart (1) and Table 8) showed unexpected promiscuous binding affinities toward 5-HT receptors (K i = 1959, 56, and 417 nm against 5-HT 2A, 5-HT 2B, and 5-HT 2C, respectively). Sorafenib was originally developed as a RAF-kinase inhibitor (IC 50 = 52 nm) but subsequently it has been shown to be a multikinase inhibitor that also inhibits PDGFR (IC 50 = 37 nm), VEGFR2 (IC 50 = 59 nm), VEGFR3 (IC 50 = 16 nm), c- Kit (IC 50 = 31 nm), and FLT1 (IC 50 = 31 nm). 5-HT 2B is highly expressed in the liver, kidneys, stomach, and gut. Considering that the 5-HT 2B binding affinity of sorafenib is in the same therapeutic window as its kinase inhibition activities, one may hypothesize that the 5-HT 2B inhibition