ArticleChemical biology & drug design2017
AVCpred: an integrated web server for prediction and design of antiviral compounds.
Article in Chemical biology & drug design, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- VITALdb: to select the best viroinformatics tools for a desired virus or application.Briefings in bioinformatics · 2025Article
- i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus.Frontiers in pharmacology · 2025Article
- Identification of natural antimicrobial peptides mimetic to inhibit CaJournal of bioenergetics and biomembranes · 2024Article
- Article
- A Computational Study of Carbazole Alkaloids from Murraya koenigii as Potential SARS-CoV-2 Main Protease Inhibitors.Applied biochemistry and biotechnology · 2023Article
- In Silico Identification of Anti-SARS-CoV-2 Medicinal Plants Using Cheminformatics and Machine Learning.Molecules (Basel, Switzerland) · 2022Article
- In Silico Prediction of Anti-Infective and Cell-Penetrating Peptides fromPharmaceuticals (Basel, Switzerland) · 2022Article
- Molecular basis for the repurposing of histamine H2-receptor antagonist to treat COVID-19.Journal of biomolecular structure & dynamics · 2022Article
- Biofilm-Molecules (Basel, Switzerland) · 2022Article
- Novel Galactopyranoside Esters: Synthesis, Mechanism, In Vitro Antimicrobial Evaluation and Molecular Docking Studies.Molecules (Basel, Switzerland) · 2022Article
- Computational Identification of Potential Multitarget Inhibitors of Nipah Virus by Molecular Docking and Molecular Dynamics.Microorganisms · 2022Article
- Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning.Molecular diversity · 2022Article
- S2DV: converting SMILES to a drug vector for predicting the activity of anti-HBV small molecules.Briefings in bioinformatics · 2022Article
- Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches.Computational and structural biotechnology journal · 2022Article
- In Silico Identification and Validation of Organic Triazole Based Ligands as Potential Inhibitory Drug Compounds of SARS-CoV-2 Main Protease.Molecules (Basel, Switzerland) · 2021Article
- Computational identification of repurposed drugs against viruses causing epidemics and pandemics via drug-target network analysis.Computers in biology and medicine · 2021Article
- Bioactivity Comparison across Multiple Machine Learning Algorithms Using over 5000 Datasets for Drug Discovery.Molecular pharmaceutics · 2021Article
- Next-generation computational tools and resources for coronavirus research: From detection to vaccine discovery.Computers in biology and medicine · 2021Review
- Structure-Based Virtual Screening to Identify Novel Potential Compound as an Alternative to Remdesivir to Overcome the RdRp Protein Mutations in SARS-CoV-2.Frontiers in molecular biosciences · 2021Article
- Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Viral infections constantly jeopardize the global public health due to lack of effective antiviral therapeutics. Therefore, there is an imperative need to speed up the drug discovery process to identify novel and efficient drug candidates. In this study, we have developed quantitative structure-activity relationship (QSAR)-based models for predicting antiviral compounds (AVCs) against deadly viruses like human immunodeficiency virus (HIV), hepatitis C virus (HCV), hepatitis B virus (HBV), human herpesvirus (HHV) and 26 others using publicly available experimental data from the ChEMBL bioactivity database. Support vector machine (SVM) models achieved a maximum Pearson correlation coefficient of 0.72, 0.74, 0.66, 0.68, and 0.71 in regression mode and a maximum Matthew's correlation coefficient 0.91, 0.93, 0.70, 0.89, and 0.71, respectively, in classification mode during 10-fold cross-validation. Furthermore, similar performance was observed on the independent validation sets. We have integrated these models in the AVCpred web server, freely available at http://crdd.osdd.net/servers/avcpred. In addition, the datasets are provided in a searchable format. We hope this web server will assist researchers in the identification of potential antiviral agents. It would also save time and cost by prioritizing new drugs against viruses before their synthesis and experimental testing.
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