scholarly journals Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace

2020 ◽  
Author(s):  
Natesh Singh ◽  
Ludovic Chaput ◽  
Bruno O Villoutreix
Keyword(s):  
Virtual Screening ◽  
Drug Repositioning ◽  
Pharmaceutical Research ◽  
Bioactive Molecules ◽  
Screening Methods ◽  
Web Servers ◽  
Web Based ◽  
Drug Candidates ◽  
Screening Experiments ◽  
Molecule Docking

Abstract 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.

scholarly journals A Free Web-Based Protocol to Assist Structure-Based Virtual Screening Experiments

2019 ◽  
Vol 20 (18) ◽  
pp. 4648 ◽  
Author(s):  
Nathalie Lagarde ◽  
Elodie Goldwaser ◽  
Tania Pencheva ◽  
Dessislava Jereva ◽  
Ilza Pajeva ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Web Service ◽  
Molecular Mechanics ◽  
In Silico ◽  
Chemical Biology ◽  
In Silico Screening ◽  
Web Based ◽  
Screening Experiments ◽  
The Web

Chemical biology and drug discovery are complex and costly processes. In silico screening approaches play a key role in the identification and optimization of original bioactive molecules and increase the performance of modern chemical biology and drug discovery endeavors. Here, we describe a free web-based protocol dedicated to small-molecule virtual screening that includes three major steps: ADME-Tox filtering (via the web service FAF-Drugs4), docking-based virtual screening (via the web service MTiOpenScreen), and molecular mechanics optimization (via the web service AMMOS2 [Automatic Molecular Mechanics Optimization for in silico Screening]). The online tools FAF-Drugs4, MTiOpenScreen, and AMMOS2 are implemented in the freely accessible RPBS (Ressource Parisienne en Bioinformatique Structurale) platform. The proposed protocol allows users to screen thousands of small molecules and to download the top 1500 docked molecules that can be further processed online. Users can then decide to purchase a small list of compounds for in vitro validation. To demonstrate the potential of this online-based protocol, we performed virtual screening experiments of 4574 approved drugs against three cancer targets. The results were analyzed in the light of published drugs that have already been repositioned on these targets. We show that our protocol is able to identify active drugs within the top-ranked compounds. The web-based protocol is user-friendly and can successfully guide the identification of new promising molecules for chemical biology and drug discovery purposes.

scholarly journals Supercomputer-aided Drug Repositioning at Scale: Virtual Screening for SARS-CoV-2 Protease Inhibitor

2020 ◽  
Author(s):  
Sangjae Seo ◽  
Jung Woo Park ◽  
Dosik An ◽  
Junwon Yoon ◽  
Hyojung Paik ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Protease Inhibitor ◽  
Binding Affinity ◽  
High Throughput ◽  
Active Site ◽  
High Throughput Screening ◽  
Drug Repositioning ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Drug Candidates

Coronavirus diseases (COVID-19) outbreak has been labelled a pandemic. For the prioritization of treatments to cope with COVID-19, it is important to conduct rapid high-throughput screening of chemical compounds to repurposing the approved drugs, such as repositioning of chloroquine (Malaria drug) for COVID-19. In this study, exploiting supercomputer resource, we conducted high-throughput virtual screening for potential repositioning candidates of the protease inhibitor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Using the three dimensional structure of main protease (Mpro) of SARS-CoV-2, we evaluated binding affinity between Mpro and drug candidates listed in SWEETLEAD library and ChEMBL database. Docking scores of 19,168 drug molecules at the active site of Mpro were calculated using Autodock Vina package. Among the calculated result, we selected 43 drug candidates and ran molecular dynamics (MD) simulation to further investigate protein-drug interaction. Among compounds that bind to the active site of SARS-CoV-2, we finally selected the 8 drugs showing the highest binding affinity; asunaprevir, atazanavir, dasabuvir, doravirine, fosamprenavir, ritonavir, voxilaprevir and amprenavir, which are the antiviral drugs of hepatitis C virus or human immunodeficiency virus. We expect that the present study provides comprehensive insights into the development of antiviral medication, especially for the treatment of COVID-19.<div><br></div><div>* Attached excel file contains a full list of results of docking calculations</div>

Current development of integrated web servers for preclinical safety and pharmacokinetics assessments in drug development

2020 ◽  
Author(s):  
Yi Hsiao ◽  
Bo-Han Su ◽  
Yufeng J Tseng
Keyword(s):  
Drug Development ◽  
In Vitro Tests ◽  
Web Servers ◽  
Web Based ◽  
Structural Modifications ◽  
Drug Candidates ◽  
Alternative Choice ◽  
Preclinical Safety

Abstract In drug development, preclinical safety and pharmacokinetics assessments of candidate drugs to ensure the safety profile are a must. While in vivo and in vitro tests are traditionally used, experimental determinations have disadvantages, as they are usually time-consuming and costly. In silico predictions of these preclinical endpoints have each been developed in the past decades. However, only a few web-based tools have integrated different models to provide a simple one-step platform to help researchers thoroughly evaluate potential drug candidates. To efficiently achieve this approach, a platform for preclinical evaluation must not only predict key ADMET (absorption, distribution, metabolism, excretion and toxicity) properties but also provide some guidance on structural modifications to improve the undesired properties. In this review, we organized and compared several existing integrated web servers that can be adopted in preclinical drug development projects to evaluate the subject of interest. We also introduced our new web server, Virtual Rat, as an alternative choice to profile the properties of drug candidates. In Virtual Rat, we provide not only predictions of important ADMET properties but also possible reasons as to why the model made those structural predictions. Multiple models were implemented into Virtual Rat, including models for predicting human ether-a-go-go-related gene (hERG) inhibition, cytochrome P450 (CYP) inhibition, mutagenicity (Ames test), blood–brain barrier penetration, cytotoxicity and Caco-2 permeability. Virtual Rat is free and has been made publicly available at https://virtualrat.cmdm.tw/.

Positive Predictive Value Surfaces as a Complementary Tool to Assess the Performance of Virtual Screening Methods

2020 ◽  
Vol 20 (14) ◽  
pp. 1447-1460
Author(s):  
Juan F. Morales ◽  
Sara Chuguransky ◽  
Lucas N. Alberca ◽  
Juan I. Alice ◽  
Sofía Goicoechea ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Positive Predictive Value ◽  
Ensemble Learning ◽  
Predictive Value ◽  
Experimental Testing ◽  
Screening Tools ◽  
Screening Methods ◽  
Learning Approaches ◽  
Classification Problems ◽  
Screening Experiments

Background: Since their introduction in the virtual screening field, Receiver Operating Characteristic (ROC) curve-derived metrics have been widely used for benchmarking of computational methods and algorithms intended for virtual screening applications. Whereas in classification problems, the ratio between sensitivity and specificity for a given score value is very informative, a practical concern in virtual screening campaigns is to predict the actual probability that a predicted hit will prove truly active when submitted to experimental testing (in other words, the Positive Predictive Value - PPV). Estimation of such probability is however, obstructed due to its dependency on the yield of actives of the screened library, which cannot be known a priori. Objective: To explore the use of PPV surfaces derived from simulated ranking experiments (retrospective virtual screening) as a complementary tool to ROC curves, for both benchmarking and optimization of score cutoff values. Methods: The utility of the proposed approach is assessed in retrospective virtual screening experiments with four datasets used to infer QSAR classifiers: inhibitors of Trypanosoma cruzi trypanothione synthetase; inhibitors of Trypanosoma brucei N-myristoyltransferase; inhibitors of GABA transaminase and anticonvulsant activity in the 6 Hz seizure model. Results: Besides illustrating the utility of PPV surfaces to compare the performance of machine learning models for virtual screening applications and to select an adequate score threshold, our results also suggest that ensemble learning provides models with better predictivity and more robust behavior. Conclusion: PPV surfaces are valuable tools to assess virtual screening tools and choose score thresholds to be applied in prospective in silico screens. Ensemble learning approaches seem to consistently lead to improved predictivity and robustness.

scholarly journals Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes

2021 ◽  
Vol 118 (51) ◽  
pp. e2112621118
Author(s):  
Joseph M. Paggi ◽  
Julia A. Belk ◽  
Scott A. Hollingsworth ◽  
Nicolas Villanueva ◽  
Alexander S. Powers ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Drug Targets ◽  
3D Structure ◽  
Three Dimensional ◽  
Rational Drug Design ◽  
Screening Methods ◽  
Pose Prediction ◽  
Learning Approaches ◽  
Sources Of Information ◽  
Drug Candidates

Over the past five decades, tremendous effort has been devoted to computational methods for predicting properties of ligands—i.e., molecules that bind macromolecular targets. Such methods, which are critical to rational drug design, fall into two categories: physics-based methods, which directly model ligand interactions with the target given the target’s three-dimensional (3D) structure, and ligand-based methods, which predict ligand properties given experimental measurements for similar ligands. Here, we present a rigorous statistical framework to combine these two sources of information. We develop a method to predict a ligand’s pose—the 3D structure of the ligand bound to its target—that leverages a widely available source of information: a list of other ligands that are known to bind the same target but for which no 3D structure is available. This combination of physics-based and ligand-based modeling improves pose prediction accuracy across all major families of drug targets. Using the same framework, we develop a method for virtual screening of drug candidates, which outperforms standard physics-based and ligand-based virtual screening methods. Our results suggest broad opportunities to improve prediction of various ligand properties by combining diverse sources of information through customized machine-learning approaches.

scholarly journals Supercomputer-aided Drug Repositioning at Scale: Virtual Screening for SARS-CoV-2 Protease Inhibitor

2020 ◽  
Author(s):  
Sangjae Seo ◽  
Jung Woo Park ◽  
Dosik An ◽  
Junwon Yoon ◽  
Hyojung Paik ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Protease Inhibitor ◽  
Binding Affinity ◽  
High Throughput ◽  
Active Site ◽  
High Throughput Screening ◽  
Drug Repositioning ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Drug Candidates

Coronavirus diseases (COVID-19) outbreak has been labelled a pandemic. For the prioritization of treatments to cope with COVID-19, it is important to conduct rapid high-throughput screening of chemical compounds to repurposing the approved drugs, such as repositioning of chloroquine (Malaria drug) for COVID-19. In this study, exploiting supercomputer resource, we conducted high-throughput virtual screening for potential repositioning candidates of the protease inhibitor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Using the three dimensional structure of main protease (Mpro) of SARS-CoV-2, we evaluated binding affinity between Mpro and drug candidates listed in SWEETLEAD library and ChEMBL database. Docking scores of 19,168 drug molecules at the active site of Mpro were calculated using Autodock Vina package. Among the calculated result, we selected 43 drug candidates and ran molecular dynamics (MD) simulation to further investigate protein-drug interaction. Among compounds that bind to the active site of SARS-CoV-2, we finally selected the 8 drugs showing the highest binding affinity; asunaprevir, atazanavir, dasabuvir, doravirine, fosamprenavir, ritonavir, voxilaprevir and amprenavir, which are the antiviral drugs of hepatitis C virus or human immunodeficiency virus. We expect that the present study provides comprehensive insights into the development of antiviral medication, especially for the treatment of COVID-19.<div><br></div><div>* Attached excel file contains a full list of results of docking calculations</div>

scholarly journals An Integrative Transcriptomic Analysis of Systemic Juvenile Idiopathic Arthritis for Identifying Potential Genetic Markers and Drug Candidates

2021 ◽  
Vol 22 (2) ◽  
pp. 712
Author(s):  
Daeun Kim ◽  
Jaeseung Song ◽  
Sora Lee ◽  
Junghyun Jung ◽  
Wonhee Jang
Keyword(s):  
Juvenile Idiopathic Arthritis ◽  
Genetic Markers ◽  
Drug Repositioning ◽  
Meta Analysis ◽  
Systemic Juvenile Idiopathic Arthritis ◽  
Gene Clusters ◽  
Bioactive Molecules ◽  
Drug Candidates ◽  
Signature Genes ◽  
Significant Gene

Systemic juvenile idiopathic arthritis (sJIA) is a rare subtype of juvenile idiopathic arthritis, whose clinical features are systemic fever and rash accompanied by painful joints and inflammation. Even though sJIA has been reported to be an autoinflammatory disorder, its exact pathogenesis remains unclear. In this study, we integrated a meta-analysis with a weighted gene co-expression network analysis (WGCNA) using 5 microarray datasets and an RNA sequencing dataset to understand the interconnection of susceptibility genes for sJIA. Using the integrative analysis, we identified a robust sJIA signature that consisted of 2 co-expressed gene sets comprising 103 up-regulated genes and 25 down-regulated genes in sJIA patients compared with healthy controls. Among the 128 sJIA signature genes, we identified an up-regulated cluster of 11 genes and a down-regulated cluster of 4 genes, which may play key roles in the pathogenesis of sJIA. We then detected 10 bioactive molecules targeting the significant gene clusters as potential novel drug candidates for sJIA using an in silico drug repositioning analysis. These findings suggest that the gene clusters may be potential genetic markers of sJIA and 10 drug candidates can contribute to the development of new therapeutic options for sJIA.

scholarly journals Pyrrolidine in Drug Discovery: A Versatile Scaffold for Novel Biologically Active Compounds

2021 ◽  
Vol 379 (5) ◽  
Author(s):  
Giovanna Li Petri ◽  
Maria Valeria Raimondi ◽  
Virginia Spanò ◽  
Ralph Holl ◽  
Paola Barraja ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Binding Mode ◽  
Biologically Active ◽  
Bioactive Molecules ◽  
Biological Profile ◽  
Pyrrolidine Ring ◽  
Reaction Conditions ◽  
Drug Candidates ◽  
Steric Factors ◽  
Proline Derivatives

AbstractThe five-membered pyrrolidine ring is one of the nitrogen heterocycles used widely by medicinal chemists to obtain compounds for the treatment of human diseases. The great interest in this saturated scaffold is enhanced by (1) the possibility to efficiently explore the pharmacophore space due to sp3-hybridization, (2) the contribution to the stereochemistry of the molecule, (3) and the increased three-dimensional (3D) coverage due to the non-planarity of the ring—a phenomenon called “pseudorotation”. In this review, we report bioactive molecules with target selectivity characterized by the pyrrolidine ring and its derivatives, including pyrrolizines, pyrrolidine-2-one, pyrrolidine-2,5-diones and prolinol described in the literature from 2015 to date. After a comparison of the physicochemical parameters of pyrrolidine with the parent aromatic pyrrole and cyclopentane, we investigate the influence of steric factors on biological activity, also describing the structure–activity relationship (SAR) of the studied compounds. To aid the reader’s approach to reading the manuscript, we have planned the review on the basis of the synthetic strategies used: (1) ring construction from different cyclic or acyclic precursors, reporting the synthesis and the reaction conditions, or (2) functionalization of preformed pyrrolidine rings, e.g., proline derivatives. Since one of the most significant features of the pyrrolidine ring is the stereogenicity of carbons, we highlight how the different stereoisomers and the spatial orientation of substituents can lead to a different biological profile of drug candidates, due to the different binding mode to enantioselective proteins. We believe that this work can guide medicinal chemists to the best approach in the design of new pyrrolidine compounds with different biological profiles.

scholarly journals Practical Model Selection for Prospective Virtual Screening

2018 ◽  
Author(s):  
Shengchao Liu ◽  
Moayad Alnammi ◽  
Spencer S. Ericksen ◽  
Andrew F. Voter ◽  
Gene E. Ananiev ◽  
...  
Keyword(s):  
Random Forest ◽  
Virtual Screening ◽  
Protein Interactions ◽  
High Throughput Screening ◽  
Screening Methods ◽  
Protein Protein Interactions ◽  
Screening Algorithm ◽  
Screening Performance ◽  
Wide Range ◽  
Public Datasets

AbstractVirtual (computational) high-throughput screening provides a strategy for prioritizing compounds for experimental screens, but the choice of virtual screening algorithm depends on the dataset and evaluation strategy. We consider a wide range of ligand-based machine learning and docking-based approaches for virtual screening on two protein-protein interactions, PriA-SSB and RMI-FANCM, and present a strategy for choosing which algorithm is best for prospective compound prioritization. Our workflow identifies a random forest as the best algorithm for these targets over more sophisticated neural network-based models. The top 250 predictions from our selected random forest recover 37 of the 54 active compounds from a library of 22,434 new molecules assayed on PriA-SSB. We show that virtual screening methods that perform well in public datasets and synthetic benchmarks, like multi-task neural networks, may not always translate to prospective screening performance on a specific assay of interest.

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