A proposal for distributed processing for large scale virtual screening using Python

In recent years, many virtual screening (VS) tools have been developed that employ different molecular representations and have different speed and accuracy characteristics. In this paper, we compare ten popular ligand-based VS tools using the publicly available Directory of Useful Decoys (DUD) data set comprising over 100 000 compounds distributed across 40 protein targets. The DUD was developed initially to evaluate docking algorithms, but our results from an operational correlation analysis show that it is also well suited for comparing ligand-based VS tools. Although it is conventional wisdom that 3D molecular shape is an important determinant of biological activity, our results based on permutational significance tests of several commonly used VS metrics show that the 2D fingerprint-based methods generally give better VS performance than the 3D shape-based approaches for surprisingly many of the DUD targets. To help understand this finding, we have analyzed the nature of the scoring functions used and the composition of the DUD data set itself. We propose that to improve the VS performance of current 3D methods, it will be necessary to devise screening queries that can represent multiple possible conformations and which can exploit knowledge of known actives that span multiple scaffold families.

Multitarget agents have been increasingly explored for enhancing efficacy and reducing countertarget activities and toxicities. Efficient virtual screening (VS) tools for searching selective multitarget agents are desired. Combinatorial support vector machines (C-SVM) were tested as VS tools for searching dual-inhibitors of 11 combinations of 9 anticancer kinase targets (EGFR, VEGFR, PDGFR, Src, FGFR, Lck, CDK1, CDK2, GSK3). C-SVM trained on 233-1,316 non-dual-inhibitors correctly identified 26.8%-57.3% (majority >36%) of the 56-230 intra-kinase-group dual-inhibitors (equivalent to the 50-70% yields of two independent individual target VS tools), and 12.2% of the 41 inter-kinase-group dual-inhibitors. C-SVM were fairly selective in misidentifying as dual-inhibitors 3.7%-48.1% (majority <20%) of the 233-1,316 non-dual-inhibitors of the same kinase pairs and 0.98%-4.77% of the 3,971-5,180 inhibitors of other kinases. C-SVM produced low false-hit rates in misidentifying as dual-inhibitors 1,746-4,817 (0.013%-0.036%) of the 13.56 M PubChem compounds, 12-175 (0.007%-0.104%) of the 168 K MDDR compounds, and 0-84 (0.0%-2.9%) of the 19,495-38,483 MDDR compounds similar to the known dual-inhibitors. C-SVM was compared to other VS methods Surflex-Dock, DOCK Blaster, kNN and PNN against the same sets of kinase inhibitors and the full set or subset of the 1.02 M Zinc clean-leads data set. C-SVM produced comparable dual-inhibitor yields, slightly better false-hit rates for kinase inhibitors, and significantly lower false-hit rates for the Zinc clean-leads data set. Combinatorial SVM showed promising potential for searching selective multitarget agents against intra-kinase-group kinases without explicit knowledge of multitarget agents.

Virtual screening (VS) is a well-established method in the initial stages of many drug and material design projects. VS is typically performed using structure-based approaches such as molecular docking, or various ligand-based approaches. Most docking tools were designed to be as global as possible, and consequently only require knowledge on the 3D structure of the biotarget. In contrast, many ligand-based approaches (e.g., 3D-QSAR and pharmacophore) require prior development of project-specific predictive models. Depending on the type of model (e.g., classification or regression), predictive ability is typically evaluated using metrics of performance on either the training set () or the test set (e.g., specificity, selectivity or ). However, none of these metrics were developed with VS in mind, and consequently, their ability to reliably assess the performances of a model in the context of VS is at best limited. With this in mind we have recently reported the development of the enrichment optimization algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations for VS by optimizing an enrichment-based metric in the space of the descriptors. Here we present an improved version of the algorithm which better handles active compounds and which also takes into account information on inactive (either known inactive or decoy) compounds. We compared the improved EOA in small-scale VS experiments with three common docking tools, namely, Glide-SP, GOLD and AutoDock Vina, employing five molecular targets (acetylcholinesterase, human immunodeficiency virus type 1 protease, MAP kinase p38 alpha, urokinase-type plasminogen activator, and trypsin I). We found that EOA consistently outperformed all docking tools in terms of the area under the ROC curve (AUC) and EF1% metrics that measured the overall and initial success of the VS process, respectively. This was the case when the docking metrics were calculated based on a consensus approach and when they were calculated based on two different sets of single crystal structures. Finally, we propose that EOA could be combined with molecular docking to derive target-specific scoring functions.

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

Postprandial hyperglycemia considered to be a major risk factor for cerebrovascular complications. The current study was designed to elucidate the beneficial role of voglibose via in-silico in vitro to in-vivo studies in improving the postprandial glycaemic state by protection against strokeprone type 2 diabetes. In-Silico molecular docking and virtual screening were carried out with the help of iGEMDOCK+ Pymol+docking software and Protein Drug Bank database (PDB). Based on the results of docking studies, in-vivo investigation was carried out for possible neuroprotective action. T2DM was induced by a single injection of streptozotocin (90mg/kg, i.v.) to neonates. Six weeks after induction, voglibose was administered at the dose of 10mg/kg p.o. for two weeks. After eight weeks, diabetic rats were subjected to middle cerebral artery occlusion, and after 72 hours of surgery, neurological deficits were determined. The blood was collected for the determination of serum glucose, CK-MB, LDH and lipid levels. Brains were excised for determination of brain infarct volume, brain hemisphere weight difference, Na+-K+ ATPase activity, ROS parameters, NO levels, and aldose reductase activity. In-silico docking studies showed good docking binding score for stroke associated proteins, which possibly hypotheses neuroprotective action of voglibose in stroke. In the present in-vivo study, pre-treatment with voglibose showed a significant decrease (p<0.05) in serum glucose and lipid levels. Voglibose has shown significant (p<0.05) reduction in neurological score, brain infarct volume, the difference in brain hemisphere weight. On biochemical evaluation, treatment with voglibose produced significant (p<0.05) decrease in CK-MB, LDH, and NO levels in blood and reduction in Na+-K+ ATPase, oxidative stress, and aldose reductase activity in brain homogenate. In-silico molecular docking and virtual screening studies and in-vivo studies in MCAo induced stroke, animal model outcomes support the strong anti-stroke signature for possible neuroprotective therapeutics.

BackgroundThe virtual screening (VS) of lead compounds using molecular docking and pharmacophore detection is now an important tool in drug discovery. VS tasks typically require a combination of several software tools and a molecular graphics system. Thus, the integration of all the requisite tools in a single operating environment could reduce the complexity of running VS experiments. However, only a few freely available integrated software platforms have been developed.ResultsA free open-source platform, IVSPlat 1.0, was developed in this study for the management and automation of VS tasks. We integrated several VS-related programs into a molecular graphics system to provide a comprehensive platform for the solution of VS tasks based on molecular docking, pharmacophore detection, and a combination of both methods. This tool can be used to visualize intermediate and final results of the VS execution, while also providing a clustering tool for the analysis of VS results. A case study was conducted to demonstrate the applicability of this platform.ConclusionsIVSPlat 1.0 provides a plug-in-based solution for the management, automation, and visualization of VS tasks. IVSPlat 1.0 is an open framework that allows the integration of extra software to extend its functionality and modified versions can be freely distributed. The open source code and documentation are available at http://kyc.nenu.edu.cn/IVSPlat/.

We have developed a new tool for virtual database screening. This tool, referred to as the Generic Evolutionary Method for molecular DOCKing (GEMDOCK), combines an evolutionary approach and a new pharmacophore-based scoring function. The former integrates discrete and continuous global search strategies with local search strategies to speed up convergence. The latter simultaneously serves as the scoring function of both molecular docking and post-docking analysis to improve the number of the true positives. We accessed the accuracy of our approach on HSV-1 thymidine kinase using a ligand database on which competing tools were evaluated. The accuracies of our predictions were 0.54 for the GH score and 1.62% for the false positive rate when the true positive rate was 100%. We found that our pharmacophore-based scoring function indeed is able to reduce the number of the false positives. These results suggest that GEMDOCK is robust and can be a useful tool for virtual database screening.

With the emergence of SARS-CoV-2 mutant strains, especially the epidemic of Omicron, it continues to evolve to strengthen immune evasion. Omicron BQ. 1 and XBB pose a serious threat to the current COVID-19 vaccine (including bivalent mRNA vaccine for mutant strains) and COVID-19-positive survivors, and all current therapeutic monoclonal antibodies are ineffective against them. Older people, those with multimorbidity, and those with specific underlying health conditions remain at increased risk of COVID-19 hospitalization and death after the initial vaccine booster. However, small-molecule drugs for conserved targets remain effective and urgently needed. The non-structural protein of SARS-CoV-2 non-structural protein 1(Nsp1) can bind to the host 40S ribosomal subunit and activate the nuclease to hydrolyze the host RNA, while the viral RNA is unaffected, thus hijacking the host system. First, the present study analyzed mutations in the Nsp1 protein and then constructed a maximum-likelihood phylogenetic tree. A virtual drug screening method based on the Nsp1 structure (Protein Data Bank ID: 7K5I) was constructed, 7495 compounds from three databases were collected for molecular docking and virtual screening, and the binding free energy was calculated by the MM/GBSA method. Our study shows that Nsp1 is relatively conserved and can be used as a comparatively fixed drug target and that therapies against Nsp1 will target all of these variants. Golvatinib, Gliquidone, and Dihydroergotamine were superior to other compounds in the crystal structure of binding conformation and free energy. All effectively interfered with Nsp1 binding to 40S protein, confirming the potential inhibitory effect of these three compounds on SARS-CoV-2. In particular, Golwatinib provides a candidate for treatment and prophylaxis in elderly patients with Omicjon, suggesting further evaluation of the anti-SARS-CoV-2 activity of these compounds in cell culture. Further studies are needed to determine the utility of this finding through prospective clinical trials and identify other meaningful drug combinations.

Multitarget agents have been extensively explored for solving limited efficacies, poor safety, and resistant profiles of an individual target. Theoretical approaches for searching and designing multitarget agents are critically useful. Here, the performance of molecular docking to search dual-target inhibitors for four kinase pairs (CDK2-GSK3B, EGFR-Src, Lck-Src, and Lck-VEGFR2) was assessed. First, the representative structures for each kinase target were chosen by structural clustering of available crystal structures. Next, the performance of molecular docking to distinguish inhibitors from noninhibitors for each individual kinase target was evaluated. The results show that molecular docking-based virtual screening illustrates good capability to find known inhibitors for individual targets, but the prediction accuracy is structurally dependent. Finally, the performance of molecular docking to identify the dual-target kinase inhibitors for four kinase pairs was evaluated. The analyses show that molecular docking successfully filters out most noninhibitors and achieves promising performance for identifying dual-kinase inhibitors for CDK2-GSK3B and Lck-VEGFR2. But a high false-positive rate leads to low enrichment of true dual-target inhibitors in the final list. This study suggests that molecular docking serves as a useful tool in searching inhibitors against dual or even multiple kinase targets, but integration with other virtual screening tools is necessary for achieving better predictions.

Virtual screening is emerging as a productive and cost-effective technology in rational drug design for the identification of novel lead compounds. An important model for virtual screening is the pharmacophore. Pharmacophore is the spatial configuration of essential features that enable a ligand molecule to interact with a specific target receptor. In the absence of a known receptor structure, a pharmacophore can be identified from a set of ligands that have been observed to interact with the target receptor. Here, we present a novel computational method for pharmacophore detection and virtual screening. The pharmacophore detection module is able to (i) align multiple flexible ligands in a deterministic manner without exhaustive enumeration of the conformational space, (ii) detect subsets of input ligands that may bind to different binding sites or have different binding modes, (iii) address cases where the input ligands have different affinities by defining weighted pharmacophores based on the number of ligands that share them, and (iv) automatically select the most appropriate pharmacophore candidates for virtual screening. The algorithm is highly efficient, allowing a fast exploration of the chemical space by virtual screening of huge compound databases. The performance of PharmaGist was successfully evaluated on a commonly used data set of G-Protein Coupled Receptor alpha1A. Additionally, a large-scale evaluation using the DUD (directory of useful decoys) data set was performed. DUD contains 2950 active ligands for 40 different receptors, with 36 decoy compounds for each active ligand. PharmaGist enrichment rates are comparable with other state-of-the-art tools for virtual screening.

Large-scale computing technologies have enabled high-throughput virtual screening involving thousands to millions of drug candidates. It is not trivial, however, for biochemical scientists to evaluate the technical alternatives and their implications for running such large experiments. Besides experience with the molecular docking tool itself, the scientist needs to learn how to run it on high-performance computing (HPC) infrastructures, and understand the impact of the choices made. Here, we review such considerations for a specific tool, AutoDock Vina, and use experimental data to illustrate the following points: (1) an additional level of parallelization increases virtual screening throughput on a multi-core machine; (2) capturing of the random seed is not enough (though necessary) for reproducibility on heterogeneous distributed computing systems; (3) the overall time spent on the screening of a ligand library can be improved by analysis of factors affecting execution time per ligand, including number of active torsions, heavy atoms and exhaustiveness. We also illustrate differences among four common HPC infrastructures: grid, Hadoop, small cluster and multi-core (virtual machine on the cloud). Our analysis shows that these platforms are suitable for screening experiments of different sizes. These considerations can guide scientists when choosing the best computing platform and set-up for their future large virtual screening experiments.Electronic supplementary materialThe online version of this article (doi:10.1007/s10822-016-9900-9) contains supplementary material, which is available to authorized users.

Inducible Nitric Oxide Synthase (iNOS) has been involved in a variety of diseases, and thus it is interesting to discover new iNOS inhibitors. This study was performed to identify natural iNOS inhibitors from traditional Chinese herbs through a combination of pharmacophore modeling, molecular docking and virtual screening. First, the pharmacophore models were generated though six known iNOS inhibitors and validated by a test database. The pharmacophore model_017 showed good performance in external validation and was employed to screen Traditional Chinese Medicine Database (Version 2009), which resulting in a hit list of 498 compounds with matching score (QFIT) above 40. Then, the hits were subjected to molecular docking for further refinement. An empirical scoring function was used to evaluate the affinity of the compounds and the target protein. Parts of compounds with high docking scores have been reported to have the related pharmacological activity from the literatures. The results provide a set of useful guidelines for the rational discovery of natural iNOS inhibitors from Chinese herbs.

Several in-house developed descriptors and our in-house docking tool ActDock were compared with virtual screening on the data set of useful decoys (DUD). The results were compared with the chemical fingerprint descriptor from ChemAxon and with the docking results of the original DUD publication. The DUD is the first published data set providing active molecules, decoys, and references for crystal structures of ligand-target complexes. The DUD was designed for the purpose of evaluating docking programs. It contains 2950 active compounds against a total of 40 target proteins. Furthermore, for every ligand the data set contains 36 structurally dissimilar decoy compounds with similar physicochemical properties. We extracted the ligands from the target proteins to extend the applicability of the data set to include ligand based virtual screening. From the 40 target proteins, 37 contained ligands that we used as query molecules for virtual screening evaluation. With this data set a large comparison was done between four different chemical fingerprints, a topological pharmacophore descriptor, the Flexophore descriptor, and ActDock. The Actelion docking tool relies on a MM2 forcefield and a pharmacophore point interaction statistic for scoring; the details are described in this publication. In terms of enrichment rates the chemical fingerprint descriptors performed better than the Flexophore and the docking tool. After removing molecules chemically similar to the query molecules the Flexophore descriptor outperformed the chemical descriptors and the topological pharmacophore descriptors. With the similarity matrix calculations used in this study it was shown that the Flexophore is well suited to find new chemical entities via "scaffold hopping". The Flexophore descriptor can be explored with a Java applet at http://www.cheminformatics.ch in the submenu Tools-->Flexophore. Its usage is free of charge and does not require registration.

BackgroundDespite continued efforts using chemical similarity methods in virtual screening, currently developed approaches suffer from time-consuming multistep procedures and low success rates. We recently developed a machine learning-based chemical binding similarity model considering common structural features from molecules binding to the same, or evolutionarily related targets. The chemical binding similarity measures the resemblance of chemical compounds in terms of binding site similarity to better describe functional similarities that arise from target binding. In this study, we have shown how the chemical binding similarity could be used in virtual screening together with the conventional structure-based methods.ResultsThe chemical binding similarity, receptor-based pharmacophore, chemical structure similarity, and molecular docking methods were evaluated to identify an effective virtual screening procedure for desired target proteins. When we tested the chemical binding similarity method with test sets of 51 kinases, it outperformed the traditional structural similarity-based methods as well as structure-based methods, such as molecular docking and receptor-based pharmacophore modeling, in terms of finding active compounds. We further validated the results by performing virtual screening (using the chemical binding similarity and receptor-based pharmacophore methods) against a completely blind dataset for mitogen-activated protein kinase kinase 1 (MEK1), ephrin type-B receptor 4 (EPHB4) and wee1-like protein kinase (WEE1). The in vitro kinase binding assay confirmed that 6 out of 13 (46.2%) for MEK1 and 2 out of 12 (16.7%) for EPHB4 were newly identified only by the chemical binding similarity model.ConclusionsWe report that the virtual screening results could further be improved by combining the chemical binding similarity model with 3D-QSAR pharmacophore and molecular docking models. Not only the new inhibitors are identified in this study, but also many of the identified molecules have low structural similarity scores against already reported inhibitors and that show the revelation of novel scaffolds.

Triple‐negative breast cancer (TNBC) faces significant treatment challenges due to its resistance to chemotherapy and high‐rate metastatic occurrence. Adjunctive treatment is a promising approach to improve chemotherapy effectiveness while reducing toxicity on normal cells. Natural compounds of Lansium domesticum (duku) exhibit potential as adjunctive cancer treatments. This study aims to investigate the potential of duku peel extract (DPE) to be developed as an adjunctive agent. The cytotoxic activity of DPE against 4T1 cells was conducted through MTT assay, both in single and combination treatments. The anti‐migratory effect of DPE was examined by scratch wound healing assay. The molecular mechanism of DPE was confirmed using virtual screening via bioinformatics approaches, including protein target prediction and molecular docking. The results show that DPE has cytotoxic activity with an IC50 value of 47 µg/mL against the 4T1 cell line, in a 24‐hour treatment period. Interestingly, DPE not only had a good synergistic effect (mean CI value, i.e. 0.34), but also showed significant inhibition of cell migration with doxorubicin (Dox). Additionally, virtual bioinformatics screening approaches suggest the potential mechanism of DPE compound action by targeting CDC25B and TOP2A. Overall, DPE holds promise as an adjunctive treatment of Dox against 4T1 TNBC cells.