Comparison of the NCI open database with seven large chemical structural databases

Insights into coordination and ligand trends of lanthanide complexes from the Cambridge Structural Database

Understanding lanthanide coordination chemistry can help develop new ligands for more efficient separation of lanthanides for critical materials needs. The Cambridge Structural Database (CSD) contains tens of thousands of single crystal structures of lanthanide complexes that can serve as a training ground for both fundamental chemical insights and future machine learning and generative artificial intelligence models. This work aims to understand the currently available structures of lanthanide complexes in CSD by analyzing the coordination shell, donor types, and ligand types, from the perspective of rare-earth element (REE) separations. We obtain four sets of lanthanide complexes from CSD: Subset 1, all Ln-containing complexes (49472 structures); Subset 2, mononuclear Ln complexes (27858 structures); Subset 3, mononuclear Ln complexes without cyclopentadienyl ligands (Cp) (26156 structures); Subset 4, Ln complexes with at least one 1,10-phenanthroline (phen) or its derivative as a coordinating ligand (2226 structures). The subsequent analysis of lanthanide complexes in these subsets examines the trends in coordination numbers and first shell distances as well as identifies and characterizes the ligands and donor groups. In addition, examples of Ln-complexes with commercially available complexants and phen-based ligands are interrogated in detail. This systematic investigation lays the groundwork for future data-driven ligand designs for REE separations based on the structural insights into the lanthanide coordination chemistry.

Consensus Pharmacophore Strategy For Identifying Novel SARS-Cov-2 Mpro Inhibitors from Large Chemical Libraries

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main Protease (Mpro) is an enzyme that cleaves viral polyproteins translated from the viral genome and is critical for viral replication. Mpro is a target for anti-SARS-CoV-2 drug development, and multiple Mpro crystals complexed with competitive inhibitors have been reported. In this study, we aimed to develop an Mpro consensus pharmacophore as a tool to expand the search for inhibitors. We generated a consensus model by aligning and summarizing pharmacophoric points from 152 bioactive conformers of SARS-CoV-2 Mpro inhibitors. Validation against a library of conformers from a subset of ligands showed that our model retrieved poses that reproduced the crystal-binding mode in 77% of the cases. Using models derived from a consensus pharmacophore, we screened >340 million compounds. Pharmacophore-matching and chemoinformatics analyses identified new potential Mpro inhibitors. The candidate compounds were chemically dissimilar to the reference set, and among them, demonstrating the relevance of our model. We evaluated the effect of 16 candidates on Mpro enzymatic activity finding that seven have inhibitory activity. Three compounds (1, 4, and 5) had IC50 values in the midmicromolar range. The Mpro consensus pharmacophore reported herein can be used to identify compounds with improved activity and novel chemical scaffolds against Mpro. The method developed for its generation is provided as an open-access code (https://github.com/AngelRuizMoreno/ConcensusPharmacophore) and can be applied to other pharmacological targets.

Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to significant global morbidity and mortality. A crucial viral protein, the non-structural protein 14 (nsp14), catalyzes the methylation of viral RNA and plays a critical role in viral genome replication and transcription. Due to the low mutation rate in the nsp region among various SARS-CoV-2 variants, nsp14 has emerged as a promising therapeutic target. However, discovering potential inhibitors remains a challenge. In this work, we introduce a computational pipeline for the rapid and efficient identification of potential nsp14 inhibitors by leveraging virtual screening and the NCI open compound collection, which contains 250,000 freely available molecules for researchers worldwide. The introduced pipeline provides a cost-effective and efficient approach for early-stage drug discovery by allowing researchers to evaluate promising molecules without incurring synthesis expenses. Our pipeline successfully identified seven promising candidates after experimentally validating only 40 compounds. Notably, we discovered NSC620333, a compound that exhibits a strong binding affinity to nsp14 with a dissociation constant of 427 ± 84 nM. In addition, we gained new insights into the structure and function of this protein through molecular dynamics simulations. We identified new conformational states of the protein and determined that residues Phe367, Tyr368, and Gln354 within the binding pocket serve as stabilizing residues for novel ligand interactions. We also found that metal coordination complexes are crucial for the overall function of the binding pocket. Lastly, we present the solved crystal structure of the nsp14-MTase complexed with SS148 (PDB:8BWU), a potent inhibitor of methyltransferase activity at the nanomolar level (IC50 value of 70 ± 6 nM). Our computational pipeline accurately predicted the binding pose of SS148, demonstrating its effectiveness and potential in accelerating drug discovery efforts against SARS-CoV-2 and other emerging viruses.

Identification of selective 5-LOX and FLAP inhibitors as novel anti-inflammatory agents by ligand-based virtual screening

Inflammation is a multifaceted biological process in which the conversion of arachidonic acid to eicosanoids, including prostaglandins and leukotrienes (LTs), plays a crucial role. 5-Lipoxygenase (5-LOX) is a key enzyme in cellular LT biosynthesis, and it is supported by the accessory protein 5-lipoxygenase-activating protein (FLAP). Pharmacological interventions to modulate LTs aim at either decreasing their biosynthesis or at mitigating their biological effects. Therefore, inhibiting 5-LOX or FLAP represents a useful strategy to reduce inflammation. Herein we present the identification and pharmacological evaluation of novel inhibitors targeting 5-LOX or FLAP. By means of a ligand-based virtual screening approach, we selected 38 compounds for in vitro assays. Among them, ALR-38 exhibits direct 5-LOX inhibition, while ALR-6 and ALR-27 showed potential as FLAP inhibitors. These latter not only reduced LT production but also promoted the generation of specialized pro-resolving mediators in specific human macrophage phenotypes. Interestingly, the identified compounds turned out to be selective for their respective targets, as none of them displayed activity towards microsomal prostaglandin E2 synthase-1 and soluble epoxide hydrolase, which are other proteins involved in eicosanoid biosynthesis. Thus, these compounds are endowed with potential therapeutic utility in mitigating inflammatory responses and might offer a venue for tackling inflammation-based disorders.

Recent advances in the self-referencing embedded strings (SELFIES) library

String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted into machine learning pipelines. However, traditional string-based representations such as SMILES are often prone to syntactic and semantic errors when produced by generative models. To address these problems, a novel representation, SELF-referencing embedded strings (SELFIES), was proposed that is inherently 100% robust, alongside an accompanying open-source implementation called selfies. Since then, we have generalized SELFIES to support a wider range of molecules and semantic constraints, and streamlined its underlying grammar. We have implemented this updated representation in subsequent versions of selfies, where we have also made major advances with respect to design, efficiency, and supported features. Hence, we present the current status of selfies (version 2.1.1) in this manuscript. Our library, selfies, is available at GitHub (https://github.com/aspuru-guzik-group/selfies).

Structure-based discovery of novel cruzain inhibitors with distinct trypanocidal activity profiles

Over 110 years after the first formal description of Chagas disease, the trypanocidal drugs thus far available have limited efficacy and several side effects. This encourages the search for novel treatments that inhibit T. cruzi targets. One of the most studied anti-T. cruzi targets is the cysteine protease cruzain; it is associated with metacyclogenesis, replication, and invasion of the host cells. We used computational techniques to identify novel molecular scaffolds that act as cruzain inhibitors. First, with a docking-based virtual screening, we identified compound 8, a competitive cruzain inhibitor with a K_i of 4.6 μM. Then, aided by molecular dynamics simulations, cheminformatics, and docking, we identified the analog compound 22 with a K_i of 27 μM. Surprisingly, despite sharing the same isoquinoline scaffold, compound 8 presented higher trypanocidal activity against the epimastigote forms, while compound 22, against the trypomastigotes and amastigotes. Taken together, compounds 8 and 22 represent a promising scaffold for further development of trypanocidal compounds as drug candidates for treating Chagas disease.

Progress and Impact of Latin American Natural Product Databases

Natural products (NPs) are a rich source of structurally novel molecules, and the chemical space they encompass is far from being fully explored. Over history, NPs have represented a significant source of bioactive molecules and have served as a source of inspiration for developing many drugs on the market. On the other hand, computer-aided drug design (CADD) has contributed to drug discovery research, mitigating costs and time. In this sense, compound databases represent a fundamental element of CADD. This work reviews the progress toward developing compound databases of natural origin, and it surveys computational methods, emphasizing chemoinformatic approaches to profile natural product databases. Furthermore, it reviews the present state of the art in developing Latin American NP databases and their practical applications to the drug discovery area.

GraphSite: Ligand Binding Site Classification with Deep Graph Learning

The binding of small organic molecules to protein targets is fundamental to a wide array of cellular functions. It is also routinely exploited to develop new therapeutic strategies against a variety of diseases. On that account, the ability to effectively detect and classify ligand binding sites in proteins is of paramount importance to modern structure-based drug discovery. These complex and non-trivial tasks require sophisticated algorithms from the field of artificial intelligence to achieve a high prediction accuracy. In this communication, we describe GraphSite, a deep learning-based method utilizing a graph representation of local protein structures and a state-of-the-art graph neural network to classify ligand binding sites. Using neural weighted message passing layers to effectively capture the structural, physicochemical, and evolutionary characteristics of binding pockets mitigates model overfitting and improves the classification accuracy. Indeed, comprehensive cross-validation benchmarks against a large dataset of binding pockets belonging to 14 diverse functional classes demonstrate that GraphSite yields the class-weighted F1-score of 81.7%, outperforming other approaches such as molecular docking and binding site matching. Further, it also generalizes well to unseen data with the F1-score of 70.7%, which is the expected performance in real-world applications. We also discuss new directions to improve and extend GraphSite in the future.

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

Exploration of the structural requirements of Aurora Kinase B inhibitors by a combined QSAR, modelling and molecular simulation approach

Aurora kinase B plays an important role in the cell cycle to orchestrate the mitotic process. The amplification and overexpression of this kinase have been implicated in several human malignancies. Therefore, Aurora kinase B is a potential drug target for anticancer therapies. Here, we combine atom-based 3D-QSAR analysis and pharmacophore model generation to identify the principal structural features of acylureidoindolin derivatives that could potentially be responsible for the inhibition of Aurora kinase B. The selected CoMFA and CoMSIA model showed significant results with cross-validation values (q²) of 0.68, 0.641 and linear regression values (r²) of 0.971, 0.933 respectively. These values support the statistical reliability of our model. A pharmacophore model was also generated, incorporating features of reported crystal complex structures of Aurora kinase B. The pharmacophore model was used to screen commercial databases to retrieve potential lead candidates. The resulting hits were analyzed at each stage for diversity based on the pharmacophore model, followed by molecular docking and filtering based on their interaction with active site residues and 3D-QSAR predictions. Subsequently, MD simulations and binding free energy calculations were performed to test the predictions and to characterize interactions at the molecular level. The results suggested that the identified compounds retained the interactions with binding residues. Binding energy decomposition identified residues Glu155, Trp156 and Ala157 of site B and Leu83 and Leu207 of site C as major contributors to binding affinity, complementary to 3D-QSAR results. To best of our knowledge, this is the first comparison of WaterSwap field and 3D-QSAR maps. Overall, this integrated strategy provides a basis for the development of new and potential AK-B inhibitors and is applicable to other protein targets.

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.

Identification and characterization of novel candidate compounds targeting 6- and 7-transmembrane μ-opioid receptor isoforms

BACKGROUND AND PURPOSE

The μ-opioid receptor (μ receptor) is the primary target for opioid analgesics. The 7-transmembrane (TM) and 6TM μ receptor isoforms mediate inhibitory and excitatory cellular effects. Here, we developed compounds selective for 6TM- or 7TM-μ receptors to further our understanding of the pharmacodynamic properties of μ receptors.

EXPERIMENTAL APPROACH

We performed virtual screening of the ZINC Drug Now library of compounds using in silico 7TM- and 6TM-μ receptor structural models and identified potential compounds that are selective for 6TM- and/or 7TM-μ receptors. Subsequently, we characterized the most promising candidate compounds in functional in vitro studies using Be2C neuroblastoma transfected cells, behavioural in vivo pain assays using various knockout mice and in ex vivo electrophysiology studies.

KEY RESULTS

Our virtual screen identified 30 potential candidate compounds. Subsequent functional in vitro cellular assays shortlisted four compounds (#5, 10, 11 and 25) that demonstrated 6TM- or 7TM-μ receptor-dependent NO release. In in vivo pain assays these compounds also produced dose-dependent hyperalgesic responses. Studies using mice that lack specific opioid receptors further established the μ receptor-dependent nature of identified novel ligands. Ex vivo electrophysiological studies on spontaneous excitatory postsynaptic currents in isolated spinal cord slices also validated the hyperalgesic properties of the most potent 6TM- (#10) and 7TM-μ receptor (#5) ligands.

CONCLUSION AND IMPLICATIONS

Our novel compounds represent a new class of ligands for μ receptors and will serve as valuable research tools to facilitate the development of opioids with significant analgesic efficacy and fewer side-effects.

Pharmacophore-based screening of diamidine small molecule inhibitors for protein arginine methyltransferases

Protein arginine methyltransferases (PRMTs) are essential epigenetic and post-translational regulators in eukaryotic organisms. Dysregulation of PRMTs is intimately related to multiple types of human diseases, particularly cancer. Based on the previously reported PRMT1 inhibitors bearing the diamidine pharmacophore, we performed virtual screening to identify additional amidine-associated structural analogs. Subsequent enzymatic tests and characterization led to the discovery of a top lead K313 (2-(4-((4-carbamimidoylphenyl)amino)phenyl)-1H-indole-6-carboximidamide), which possessed low-micromolar potency with biochemical IC50 of 2.6 μM for human PRMT1. Limited selectivity was observed over some other PRMT isoforms such as CARM1 and PRMT7. Molecular modeling and inhibition pattern studies suggest that K313 is a nonclassic noncompetitive inhibitor to PRMT1. K313 significantly inhibited cell proliferation and reduced the arginine asymmetric dimethylation level in the leukaemia cancer cells.

Discovery of Benzopyridone-Based Transient Receptor Potential Vanilloid 1 Agonists and Antagonists and the Structural Elucidation of Their Activity Shift

Among a series of benzopyridone-based scaffolds investigated as human transient receptor potential vanilloid 1 (TRPV1) ligands, two isomeric benzopyridone scaffolds demonstrated a consistent and distinctive functional profile in which 2-oxo-1,2-dihydroquinolin-5-yl analogues (e.g., 2) displayed high affinity and potent antagonism, whereas 1-oxo-1,2-dihydroisoquinolin-5-yl analogues (e.g., 3) showed full agonism with high potency. Our computational models provide insight into the agonist-antagonist boundary of the analogues suggesting that the Arg557 residue in the S4-S5 linker might be important for sensing the agonist binding and transmitting signals. These results provide structural insights into the TRPV1 and the protein-ligand interactions at a molecular level.

Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling

Advancing a new drug to market requires substantial investments in time as well as financial resources. Crucial bioactivities for drug candidates, including their efficacy, pharmacokinetics (PK), and adverse effects, need to be investigated during drug development. With advancements in chemical synthesis and biological screening technologies over the past decade, a large amount of biological data points for millions of small molecules have been generated and are stored in various databases. These accumulated data, combined with new machine learning (ML) approaches, such as deep learning, have shown great potential to provide insights into relevant chemical structures to predict in vitro, in vivo, and clinical outcomes, thereby advancing drug discovery and development in the big data era.

In Silico Strategies in Tuberculosis Drug Discovery

Tuberculosis (TB) remains a serious threat to global public health, responsible for an estimated 1.5 million mortalities in 2018. While there are available therapeutics for this infection, slow-acting drugs, poor patient compliance, drug toxicity, and drug resistance require the discovery of novel TB drugs. Discovering new and more potent antibiotics that target novel TB protein targets is an attractive strategy towards controlling the global TB epidemic. In silico strategies can be applied at multiple stages of the drug discovery paradigm to expedite the identification of novel anti-TB therapeutics. In this paper, we discuss the current TB treatment, emergence of drug resistance, and the effective application of computational tools to the different stages of TB drug discovery when combined with traditional biochemical methods. We will also highlight the strengths and points of improvement in in silico TB drug discovery research, as well as possible future perspectives in this field.

Freely Accessible Chemical Database Resources of Compounds for In Silico Drug Discovery

BACKGROUND

In silico drug discovery has been proved to be a solidly established key component in early drug discovery. However, this task is hampered by the limitation of quantity and quality of compound databases for screening. In order to overcome these obstacles, freely accessible database resources of compounds have bloomed in recent years. Nevertheless, how to choose appropriate tools to treat these freely accessible databases is crucial. To the best of our knowledge, this is the first systematic review on this issue.

OBJECTIVE

The existed advantages and drawbacks of chemical databases were analyzed and summarized based on the collected six categories of freely accessible chemical databases from literature in this review.

RESULTS

Suggestions on how and in which conditions the usage of these databases could be reasonable were provided. Tools and procedures for building 3D structure chemical libraries were also introduced.

CONCLUSION

In this review, we described the freely accessible chemical database resources for in silico drug discovery. In particular, the chemical information for building chemical database appears as attractive resources for drug design to alleviate experimental pressure.

Computational approaches for identifying potential inhibitors on targeting protein interactions in drug discovery

In the era of big data, the interplay of artificial and human intelligence is the demanding job to address the concerns involving exchange of decisions between both sides. Drug discovery is one of the key sources of the big data, which involves synergy among various computational methods to achieve a clinical success. Rightful acquisition, mining and analysis of the data related to ligand and targets are crucial to accomplish reliable outcomes in the entire process. Novel designing and screening tactics are necessary to substantiate a potent and efficient lead compounds. Such methods are emphasized and portrayed in the current review targeting protein-ligand and protein-protein interactions involved in various diseases with potential applications.

Fragment-Based Computational Method for Designing GPCR Ligands

G protein-coupled receptors (GPCRs) are the largest family of cell surface receptors, which is arguably the most important family of drug target. With the technology breakthroughs in X-ray crystallography and cryo-electron microscopy, more than 300 GPCR-ligand complex structures have been publicly reported since 2007, covering about 60 unique GPCRs. Such abundant structural information certainly will facilitate the structure-based drug design by targeting GPCRs. In this study, we have developed a fragment-based computational method for designing novel GPCR ligands. We first extracted the characteristic interaction patterns (CIPs) on the binding interfaces between GPCRs and their ligands. The CIPs were used as queries to search the chemical fragments derived from GPCR ligands, which were required to form similar interaction patterns with GPCR. Then, the selected chemical fragments were assembled into complete molecules by using the AutoT&T2 software. In this work, we chose β-adrenergic receptor (β-AR) and muscarinic acetylcholine receptor (mAChR) as the targets to validate this method. Based on the designs suggested by our method, samples of 63 compounds were purchased and tested in a cell-based functional assay. A total of 15 and 22 compounds were identified as active antagonists for β₂-AR and mAChR M1, respectively. Molecular dynamics simulations and binding free energy analysis were performed to explore the key interactions (e.g., hydrogen bonds and π-π interactions) between those active compounds and their target GPCRs. In summary, our work presents a useful approach to the de novo design of GPCR ligands based on the relevant 3D structural information.

2,4-Thiazolidinediones as PTP 1B Inhibitors: A Mini Review (2012-2018)

2,4-thiazolidinedione (TZD) scaffold is a synthetic versatile scaffold explored by medicinal chemists for the discovery of novel molecules for the target-specific approach to treat or manage number of deadly ailments. PTP 1B is the negative regulator of insulin signaling cascade, and its diminished activity results in abolishment of insulin resistance associated with T2DM. The present review focused on the seven years journey (2012-2018) of TZDs as PTP 1B inhibitors with the insight into the amendments in the structural framework of TZD scaffold in order to optimize/design potential PTP 1B inhibitors. We have investigated the synthesized molecules based on TZD scaffold with potential activity profile against PTP 1B. Based on the SAR studies, the combined essential pharmacophoric features of selective and potent TZDs have been mapped and presented herewith for further design and synthesis of novel inhibitors of PTP 1B. Compound 46 bearing TZD scaffold with N-methyl benzoic acid and 5-(3-methoxy-4-phenethoxy) benzylidene exhibited the most potent activity (IC50 1.1 µM). Imidazolidine-2,4-dione, isosteric analogue of TZD, substituted with 1-(2,4-dichlorobenzyl)-5-(3-(2,4- dichlorobenzyloxy)benzylidene) (Compound 15) also endowed with very good PTP inhibitory activity profile (IC50 0.57 µM). It is noteworthy that Z-configuration is essential in structural framework around the double bond of arylidene for the designing of bi-dentate ligands with optimum activity.

Automated partial atomic charge assignment for drug-like molecules: a fast knapsack approach

A key factor in computational drug design is the consistency and reliability with which intermolecular interactions between a wide variety of molecules can be described. Here we present a procedure to efficiently, reliably and automatically assign partial atomic charges to atoms based on known distributions. We formally introduce the molecular charge assignment problem, where the task is to select a charge from a set of candidate charges for every atom of a given query molecule. Charges are accompanied by a score that depends on their observed frequency in similar neighbourhoods (chemical environments) in a database of previously parameterised molecules. The aim is to assign the charges such that the total charge equals a known target charge within a margin of error while maximizing the sum of the charge scores. We show that the problem is a variant of the well-studied multiple-choice knapsack problem and thus weakly \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {NP}$$\end{document}NP-complete. We propose solutions based on Integer Linear Programming and a pseudo-polynomial time Dynamic Programming algorithm. We demonstrate that the results obtained for novel molecules not included in the database are comparable to the ones obtained performing explicit charge calculations while decreasing the time to determine partial charges for a molecule from hours or even days to below a second. Our software is openly available.

A combinatorial approach of structure-based virtual screening and molecular dynamics simulation towards the discovery of a highly selective inhibitor for VP9 coat protein of Banna virus

Structure based virtual screening of two libraries containing 27,628 numbers of antiviral compounds was used to discover a few of the potent inhibitor molecules against Banna virus (BAV). Cross-docking studies with many common interfering proteins provided five of the highly selective inhibitor for BAV. Analyses of the leading molecules with ADME-Tox filtering tool and atomistic molecular dynamics simulation studies finally discovered a benzoxazolone derivative as one of the most promising molecules towards the highly selective inhibition of BAV. The theoretical calculations are also supported by the experimental evidences where the interactions between the hit ligand and a model peptide sequence, mimicking the VP9 protein of BAV, were studied. Overall the development of a personalized therapeutic towards the highly selective inhibition of BAV is discussed herein for the first time in literature.

eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates

BACKGROUND

The efficiency of drug development defined as a number of successfully launched new pharmaceuticals normalized by financial investments has significantly declined. Nonetheless, recent advances in high-throughput experimental techniques and computational modeling promise reductions in the costs and development times required to bring new drugs to market. The prediction of toxicity of drug candidates is one of the important components of modern drug discovery.

RESULTS

In this work, we describe eToxPred, a new approach to reliably estimate the toxicity and synthetic accessibility of small organic compounds. eToxPred employs machine learning algorithms trained on molecular fingerprints to evaluate drug candidates. The performance is assessed against multiple datasets containing known drugs, potentially hazardous chemicals, natural products, and synthetic bioactive compounds. Encouragingly, eToxPred predicts the synthetic accessibility with the mean square error of only 4% and the toxicity with the accuracy of as high as 72%.

CONCLUSIONS

eToxPred can be incorporated into protocols to construct custom libraries for virtual screening in order to filter out those drug candidates that are potentially toxic or would be difficult to synthesize. It is freely available as a stand-alone software at https://github.com/pulimeng/etoxpred .

Development and Testing of Druglike Screening Libraries

Although significant advances in experimental high throughput screening (HTS) have been made for drug lead identification, in silico virtual screening (VS) is indispensable owing to its unique advantage over experimental HTS, target-focused, cheap, and efficient, albeit its disadvantage of producing false positive hits. For both experimental HTS and VS, the quality of screening libraries is crucial and determines the outcome of those studies. In this paper, we first reviewed the recent progress on screening library construction. We realized the urgent need for compiling high-quality screening libraries in drug discovery. Then we compiled a set of screening libraries from about 20 million druglike ZINC molecules by running fingerprint-based similarity searches against known drug molecules. Lastly, the screening libraries were objectively evaluated using 5847 external actives covering more than 2000 drug targets. The result of the assessment is very encouraging. For example, with the Tanimoto coefficient being set to 0.75, 36% of external actives were retrieved and the enrichment factor was 13. Additionally, drug target family specific screening libraries were also constructed and evaluated. The druglike screening libraries are available for download from https://mulan.pharmacy.pitt.edu .

In silico insights on tankyrase protein: A potential target for colorectal cancer

The Wnt/β-catenin pathway plays an important regulatory role in cancer signaling and cell regenerative mechanisms. Its suppression has long been considered as an important challenge of anticancer treatment and management. The poly(ADP-ribose) polymerase (PARP) family represented as a new class of therapeutic targets with diverse potential disease indications. Tankyrase (TNKS) is considered to be a potential target for the intervention of various cancers. The main objective of the work is to explore the molecular and quantum mechanics of the drug-like compounds and to identify the potential inhibitors for TNKS protein using the structure and ligand-based virtual screening from several databases and to explore the binding pocket and interactions of active residues. The screened compounds were further filtered using binding-free energy calculation and molecular dynamics simulation studies. The results have provided a strong molecular knowledge of TNKS and offered top hit potent inhibitors. The identified lead compounds LC_40781, LC_40777, LC_39767, LC_8346, NCI_682438, and NCI_721141 were observed to have potent activity against TNKS protein. The hydrogen bonding of compounds with Asp1198, His1201, Tyr1203 in TNKS1 and Gly1032, Ser1068 in TNKS2 are the key interactions plays a major role in binding energy. Therefore, the outcome of the study would help for further validation and provides valuable information to guide the future TNKS-specific inhibitor designing. Communicated by Ramaswamy H. Sarma.

Comparative assessment of strategies to identify similar ligand-binding pockets in proteins

Background

Detecting similar ligand-binding sites in globally unrelated proteins has a wide range of applications in modern drug discovery, including drug repurposing, the prediction of side effects, and drug-target interactions. Although a number of techniques to compare binding pockets have been developed, this problem still poses significant challenges.

Results

We evaluate the performance of three algorithms to calculate similarities between ligand-binding sites, APoc, SiteEngine, and G-LoSA. Our assessment considers not only the capabilities to identify similar pockets and to construct accurate local alignments, but also the dependence of these alignments on the sequence order. We point out certain drawbacks of previously compiled datasets, such as the inclusion of structurally similar proteins, leading to an overestimated performance. To address these issues, a rigorous procedure to prepare unbiased, high-quality benchmarking sets is proposed. Further, we conduct a comparative assessment of techniques directly aligning binding pockets to indirect strategies employing structure-based virtual screening with AutoDock Vina and rDock.

Conclusions

Thorough benchmarks reveal that G-LoSA offers a fairly robust overall performance, whereas the accuracy of APoc and SiteEngine is satisfactory only against easy datasets. Moreover, combining various algorithms into a meta-predictor improves the performance of existing methods to detect similar binding sites in unrelated proteins by 5–10%. All data reported in this paper are freely available at https://osf.io/6ngbs/.

Ligand-Based Pharmacophore Screening Strategy: a Pragmatic Approach for Targeting HER Proteins

Targeting ErbB family of receptors is an important therapeutic option, because of its essential role in the broad spectrum of human cancers, including non-small cell lung cancer (NSCLC). Therefore, in the present work, considerable effort has been made to develop an inhibitor against HER family proteins, by combining the use of pharmacophore modelling, docking scoring functions, and ADME property analysis. Initially, a five-point pharmacophore model was developed using known HER family inhibitors. The generated model was then used as a query to screen a total of 468,880 compounds of three databases namely ZINC, ASINEX, and DrugBank. Subsequently, docking analysis was carried out to obtain hit molecules that could inhibit the HER receptors. Further, analysis of GLIDE scores and ADME properties resulted in one hit namely BAS01025917 with higher glide scores, increased CNS involvement, and good pharmaceutically relevant properties than reference ligand, afatinib. Furthermore, the inhibitory activity of the lead compounds was validated by performing molecular dynamic simulations. Of note, BAS01025917 was found to possess scaffolds with a broad spectrum of antitumor activity. We believe that this novel hit molecule can be further exploited for the development of a pan-HER inhibitor with low toxicity and greater potential.

Vitamin B12 ameliorates the phenotype of a mouse model of DiGeorge syndrome

Abstract

Pathological conditions caused by reduced dosage of a gene, such as gene haploinsufficiency, can potentially be reverted by enhancing the expression of the functional allele. In practice, low specificity of therapeutic agents, or their toxicity reduces their clinical applicability. Here, we have used a high throughput screening (HTS) approach to identify molecules capable of increasing the expression of the gene Tbx1, which is involved in one of the most common gene haploinsufficiency syndromes, the 22q11.2 deletion syndrome. Surprisingly, we found that one of the two compounds identified by the HTS is the vitamin B12. Validation in a mouse model demonstrated that vitamin B12 treatment enhances Tbx1 gene expression and partially rescues the haploinsufficiency phenotype. These results lay the basis for preclinical and clinical studies to establish the effectiveness of this drug in the human syndrome.

Ensemble docking to difficult targets in early-stage drug discovery: Methodology and application to fibroblast growth factor 23

Ensemble docking is now commonly used in early-stage in silico drug discovery and can be used to attack difficult problems such as finding lead compounds which can disrupt protein-protein interactions. We give an example of this methodology here, as applied to fibroblast growth factor 23 (FGF23), a protein hormone that is responsible for regulating phosphate homeostasis. The first small-molecule antagonists of FGF23 were recently discovered by combining ensemble docking with extensive experimental target validation data (Science Signaling, 9, 2016, ra113). Here, we provide a detailed account of how ensemble-based high-throughput virtual screening was used to identify the antagonist compounds discovered in reference (Science Signaling, 9, 2016, ra113). Moreover, we perform further calculations, redocking those antagonist compounds identified in reference (Science Signaling, 9, 2016, ra113) that performed well on drug-likeness filters, to predict possible binding regions. These predicted binding modes are rescored with the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) approach to calculate the most likely binding site. Our findings suggest that the antagonist compounds antagonize FGF23 through the disruption of protein-protein interactions between FGF23 and fibroblast growth factor receptor (FGFR).

Virtual Ligand Screening Using PL-PatchSurfer2, a Molecular Surface-Based Protein-Ligand Docking Method

Virtual screening is a computational technique for predicting a potent binding compound for a receptor protein from a ligand library. It has been a widely used in the drug discovery field to reduce the efforts of medicinal chemists to find hit compounds by experiments.Here, we introduce our novel structure-based virtual screening program, PL-PatchSurfer, which uses molecular surface representation with the three-dimensional Zernike descriptors, which is an effective mathematical representation for identifying physicochemical complementarities between local surfaces of a target protein and a ligand. The advantage of the surface-patch description is its tolerance on a receptor and compound structure variation. PL-PatchSurfer2 achieves higher accuracy on apo form and computationally modeled receptor structures than conventional structure-based virtual screening programs. Thus, PL-PatchSurfer2 opens up an opportunity for targets that do not have their crystal structures. The program is provided as a stand-alone program at http://kiharalab.org/plps2 . We also provide files for two ligand libraries, ChEMBL and ZINC Drug-like.

An integrative in-silico approach for therapeutic target identification in the human pathogen Corynebacterium diphtheriae

Corynebacterium diphtheriae (Cd) is a Gram-positive human pathogen responsible for diphtheria infection and once regarded for high mortalities worldwide. The fatality gradually decreased with improved living standards and further alleviated when many immunization programs were introduced. However, numerous drug-resistant strains emerged recently that consequently decreased the efficacy of current therapeutics and vaccines, thereby obliging the scientific community to start investigating new therapeutic targets in pathogenic microorganisms. In this study, our contributions include the prediction of modelome of 13 C. diphtheriae strains, using the MHOLline workflow. A set of 463 conserved proteins were identified by combining the results of pangenomics based core-genome and core-modelome analyses. Further, using subtractive proteomics and modelomics approaches for target identification, a set of 23 proteins was selected as essential for the bacteria. Considering human as a host, eight of these proteins (glpX, nusB, rpsH, hisE, smpB, bioB, DIP1084, and DIP0983) were considered as essential and non-host homologs, and have been subjected to virtual screening using four different compound libraries (extracted from the ZINC database, plant-derived natural compounds and Di-terpenoid Iso-steviol derivatives). The proposed ligand molecules showed favorable interactions, lowered energy values and high complementarity with the predicted targets. Our proposed approach expedites the selection of C. diphtheriae putative proteins for broad-spectrum development of novel drugs and vaccines, owing to the fact that some of these targets have already been identified and validated in other organisms.

Structure-Based Target-Specific Screening Leads to Small-Molecule CaMKII Inhibitors

Target-specific scoring methods are more commonly used to identify small-molecule inhibitors among compounds docked to a target of interest. Top candidates that emerge from these methods have rarely been tested for activity and specificity across a family of proteins. In this study we docked a chemical library into CaMKIIδ, a member of the Ca2+ /calmodulin (CaM)-dependent protein kinase (CaMK) family, and re-scored the resulting protein-compound structures using Support Vector Machine SPecific (SVMSP), a target-specific method that we developed previously. Among the 35 selected candidates, three hits were identified, such as quinazoline compound 1 (KIN-1; N4-[7-chloro-2-[(E)-styryl]quinazolin-4-yl]-N1,N1-diethylpentane-1,4-diamine), which was found to inhibit CaMKIIδ kinase activity at single-digit micromolar IC50 . Activity across the kinome was assessed by profiling analogues of 1, namely 6 (KIN-236; N4-[7-chloro-2-[(E)-2-(2-chloro-4,5-dimethoxyphenyl)vinyl]quinazolin-4-yl]-N1,N1-diethylpentane-1,4-diamine), and an analogue of hit compound 2 (KIN-15; 2-[4-[(E)-[(5-bromobenzofuran-2-carbonyl)hydrazono]methyl]-2-chloro-6-methoxyphenoxy]acetic acid), namely 14 (KIN-332; N-[(E)-[4-(2-anilino-2-oxoethoxy)-3-chlorophenyl]methyleneamino]benzofuran-2-carboxamide), against 337 kinases. Interestingly, for compound 6, CaMKIIδ and homologue CaMKIIγ were among the top ten targets. Among the top 25 targets of 6, IC50 values ranged from 5 to 22 μm. Compound 14 was found to be not specific toward CaMKII kinases, but it does inhibit two kinases with sub-micromolar IC50 values among the top 25. Derivatives of 1 were tested against several kinases including several members of the CaMK family. These data afforded a limited structure-activity relationship study. Molecular dynamics simulations with explicit solvent followed by end-point MM-GBSA free-energy calculations revealed strong engagement of specific residues within the ATP binding pocket, and also changes in the dynamics as a result of binding. This work suggests that target-specific scoring approaches such as SVMSP may hold promise for the identification of small-molecule kinase inhibitors that exhibit some level of specificity toward the target of interest across a large number of proteins.

The influence of the negative-positive ratio and screening database size on the performance of machine learning-based virtual screening

The machine learning-based virtual screening of molecular databases is a commonly used approach to identify hits. However, many aspects associated with training predictive models can influence the final performance and, consequently, the number of hits found. Thus, we performed a systematic study of the simultaneous influence of the proportion of negatives to positives in the testing set, the size of screening databases and the type of molecular representations on the effectiveness of classification. The results obtained for eight protein targets, five machine learning algorithms (SMO, Naïve Bayes, Ibk, J48 and Random Forest), two types of molecular fingerprints (MACCS and CDK FP) and eight screening databases with different numbers of molecules confirmed our previous findings that increases in the ratio of negative to positive training instances greatly influenced most of the investigated parameters of the ML methods in simulated virtual screening experiments. However, the performance of screening was shown to also be highly dependent on the molecular library dimension. Generally, with the increasing size of the screened database, the optimal training ratio also increased, and this ratio can be rationalized using the proposed cost-effectiveness threshold approach. To increase the performance of machine learning-based virtual screening, the training set should be constructed in a way that considers the size of the screening database.

Identification by High-Throughput Screening of Pseudomonas Acyl-Coenzyme A Synthetase Inhibitors

Pseudomonas infections are common among hospitalized, immunocompromised, and chronic lung disease patients. These infections are recalcitrant to common antibacterial therapies due to inherent antibiotic resistance. To meet the need of new anti- Pseudomonas drugs, a sensitive, homogenous, and robust assay was developed with the aim of identifying inhibitors of acyl-coenzyme A synthetases (ACSs) from Pseudomonas. Given the importance of fatty acids for in vivo nutrition of Pseudomonas, such inhibitors might have the potential to reduce the bacterial fitness during infection. The assay, based on a coupled reaction between the Pseudomonas spp. ACS and the firefly luciferase, allowed the identification of three classes of inhibitors by screening of a diverse compound collection. These compounds were confirmed to reversibly bind ACS with potencies in the micromolar range. Two classes were found to compete with acyl-coenzyme A, while the third one was competitive with fatty acid binding. Although these compounds inhibit the bacterial ACS in cell-free assays, they show modest or no effect on Pseudomonas growth in vitro.

Quo vadis G protein-coupled receptor ligands? A tool for analysis of the emergence of new groups of compounds over time

Exponential growth in the number of compounds with experimentally verified activity towards particular target has led to the emergence of various databases gathering data on biological activity. In this study, the ligands of family A of the G Protein-Coupled Receptors that are collected in the ChEMBL database were examined, and special attention was given to serotonin receptors. Sets of compounds were examined in terms of their appearance over time, they were mapped to the chemical space of drugs deposited in DrugBank, and the emergence of structurally new clusters of compounds was indicated. In addition, a tool for detailed analysis of the obtained visualizations was prepared and made available online at http://chem.gmum.net/vischem, which enables the investigation of chemical structures while referring to particular data points depicted in the figures and changes in compounds datasets over time.

Virtual Screening Approaches towards the Discovery of Toll-Like Receptor Modulators

This review aims to summarize the latest efforts performed in the search for novel chemical entities such as Toll-like receptor (TLR) modulators by means of virtual screening techniques. This is an emergent research field with only very recent (and successful) contributions. Identification of drug-like molecules with potential therapeutic applications for the treatment of a variety of TLR-regulated diseases has attracted considerable interest due to the clinical potential. Additionally, the virtual screening databases and computational tools employed have been overviewed in a descriptive way, widening the scope for researchers interested in the field.

A graph-based approach to construct target-focused libraries for virtual screening

Background

Due to exorbitant costs of high-throughput screening, many drug discovery projects commonly employ inexpensive virtual screening to support experimental efforts. However, the vast majority of compounds in widely used screening libraries, such as the ZINC database, will have a very low probability to exhibit the desired bioactivity for a given protein. Although combinatorial chemistry methods can be used to augment existing compound libraries with novel drug-like compounds, the broad chemical space is often too large to be explored. Consequently, the trend in library design has shifted to produce screening collections specifically tailored to modulate the function of a particular target or a protein family.

Methods

Assuming that organic compounds are composed of sets of rigid fragments connected by flexible linkers, a molecule can be decomposed into its building blocks tracking their atomic connectivity. On this account, we developed eSynth, an exhaustive graph-based search algorithm to computationally synthesize new compounds by reconnecting these building blocks following their connectivity patterns.

Results

We conducted a series of benchmarking calculations against the Directory of Useful Decoys, Enhanced database. First, in a self-benchmarking test, the correctness of the algorithm is validated with the objective to recover a molecule from its building blocks. Encouragingly, eSynth can efficiently rebuild more than 80 % of active molecules from their fragment components. Next, the capability to discover novel scaffolds is assessed in a cross-benchmarking test, where eSynth successfully reconstructed 40 % of the target molecules using fragments extracted from chemically distinct compounds. Despite an enormous chemical space to be explored, eSynth is computationally efficient; half of the molecules are rebuilt in less than a second, whereas 90 % take only about a minute to be generated.

Conclusions

eSynth can successfully reconstruct chemically feasible molecules from molecular fragments. Furthermore, in a procedure mimicking the real application, where one expects to discover novel compounds based on a small set of already developed bioactives, eSynth is capable of generating diverse collections of molecules with the desired activity profiles. Thus, we are very optimistic that our effort will contribute to targeted drug discovery. eSynth is freely available to the academic community at www.brylinski.org/content/molecular-synthesis.Graphical abstract

Discovery of Novel Nonactive Site Inhibitors of the Prothrombinase Enzyme Complex

The risk of serious bleeding is a major liability of anticoagulant drugs that are active-site competitive inhibitors targeting the Factor Xa (FXa) prothrombin (PT) binding site. The present work identifies several new classes of small molecule anticoagulants that can act as nonactive site inhibitors of the prothrombinase (PTase) complex composed of FXa and Factor Va (FVa). These new classes of anticoagulants were identified, using a novel agnostic computational approach to identify previously unrecognized binding pockets at the FXa-FVa interface. From about three million docking calculations of 281,128 compounds in a conformational ensemble of FXa heavy chains identified by molecular dynamics (MD) simulations, 97 compounds and their structural analogues were selected for experimental validation, through a series of inhibition assays. The compound selection was based on their predicted binding affinities to FXa and their ability to successfully bind to multiple protein conformations while showing selectivity for particular binding sites at the FXa/FVa interface. From these, thirty-one (31) compounds were experimentally identified as nonactive site inhibitors. Concentration-based assays further identified 10 compounds represented by four small-molecule families of inhibitors that achieve dose-independent partial inhibition of PTase activity in a nonactive site-dependent and self-limiting mechanism. Several compounds were identified for their ability to bind to protein conformations only seen during MD, highlighting the importance of accounting for protein flexibility in structure-based drug discovery approaches.

Binding site druggability assessment in fragment-based drug design

Target druggability refers to the propensity that a particular target is amenable to bind high-affinity drug-like molecules. A robust yet accurate computational assessment of target druggability would greatly benefit the fields of chemical genomics and drug discovery. Here, we illustrate a structure-based computational protocol to quantitatively assess the target binding-site druggability via in silico screening a fragment-like compound library. In particular, we provide guidelines, suggestions, and critical thoughts on different aspects of this computational protocol, including: construction of fragment library, preparation of target structure, in silico fragment screening, and analysis of druggability.

Proteome scale comparative modeling for conserved drug and vaccine targets identification in Corynebacterium pseudotuberculosis

Corynebacterium pseudotuberculosis (Cp) is a pathogenic bacterium that causes caseous lymphadenitis (CLA), ulcerative lymphangitis, mastitis, and edematous to a broad spectrum of hosts, including ruminants, thereby threatening economic and dairy industries worldwide. Currently there is no effective drug or vaccine available against Cp. To identify new targets, we adopted a novel integrative strategy, which began with the prediction of the modelome (tridimensional protein structures for the proteome of an organism, generated through comparative modeling) for 15 previously sequenced C. pseudotuberculosis strains. This pan-modelomics approach identified a set of 331 conserved proteins having 95-100% intra-species sequence similarity. Next, we combined subtractive proteomics and modelomics to reveal a set of 10 Cp proteins, which may be essential for the bacteria. Of these, 4 proteins (tcsR, mtrA, nrdI, and ispH) were essential and non-host homologs (considering man, horse, cow and sheep as hosts) and satisfied all criteria of being putative targets. Additionally, we subjected these 4 proteins to virtual screening of a drug-like compound library. In all cases, molecules predicted to form favorable interactions and which showed high complementarity to the target were found among the top ranking compounds. The remaining 6 essential proteins (adk, gapA, glyA, fumC, gnd, and aspA) have homologs in the host proteomes. Their active site cavities were compared to the respective cavities in host proteins. We propose that some of these proteins can be selectively targeted using structure-based drug design approaches (SBDD). Our results facilitate the selection of C. pseudotuberculosis putative proteins for developing broad-spectrum novel drugs and vaccines. A few of the targets identified here have been validated in other microorganisms, suggesting that our modelome strategy is effective and can also be applicable to other pathogens.

Scaffold hopping approach on the route to selective tankyrase inhibitors

A virtual screening procedure was applied to identify new tankyrase inhibitors. Through pharmacophore screening of a compounds collection from the SPECS database, the methoxy[l]benzothieno[2,3-c]quinolin-6(5H)-one scaffold was identified as nicotinamide mimetic able to inhibit tankyrase activity at low micromolar concentration. In order to improve potency and selectivity, tandem structure-based and scaffold hopping approaches were carried out over the new scaffold leading to the discovery of the 2-(phenyl)-3H-benzo[4,5]thieno[3,2-d]pyrimidin-4-one as powerful chemotype suitable for tankyrase inhibition. The best compound 2-(4-tert-butyl-phenyl)-3H-benzo[4,5]thieno[3,2-d]pyrimidin-4-one (23) displayed nanomolar potencies (IC50s TNKS-1 = 21 nM and TNKS-2 = 29 nM) and high selectivity when profiled against several other PARPs. Furthermore, a striking Wnt signaling, as well as cell growth inhibition, was observed assaying 23 in DLD-1 cancer cells.

eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models

Detecting similarities between ligand binding sites in the absence of global homology between target proteins has been recognized as one of the critical components of modern drug discovery. Local binding site alignments can be constructed using sequence order-independent techniques, however, to achieve a high accuracy, many current algorithms for binding site comparison require high-quality experimental protein structures, preferably in the bound conformational state. This, in turn, complicates proteome scale applications, where only various quality structure models are available for the majority of gene products. To improve the state-of-the-art, we developed eMatchSite, a new method for constructing sequence order-independent alignments of ligand binding sites in protein models. Large-scale benchmarking calculations using adenine-binding pockets in crystal structures demonstrate that eMatchSite generates accurate alignments for almost three times more protein pairs than SOIPPA. More importantly, eMatchSite offers a high tolerance to structural distortions in ligand binding regions in protein models. For example, the percentage of correctly aligned pairs of adenine-binding sites in weakly homologous protein models is only 4–9% lower than those aligned using crystal structures. This represents a significant improvement over other algorithms, e.g. the performance of eMatchSite in recognizing similar binding sites is 6% and 13% higher than that of SiteEngine using high- and moderate-quality protein models, respectively. Constructing biologically correct alignments using predicted ligand binding sites in protein models opens up the possibility to investigate drug-protein interaction networks for complete proteomes with prospective systems-level applications in polypharmacology and rational drug repositioning. eMatchSite is freely available to the academic community as a web-server and a stand-alone software distribution at http://www.brylinski.org/ematchsite.