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Bioinformatics. 2019 Aug 16. pii: btz638. doi: 10.1093/bioinformatics/btz638. [Epub ahead of print]

AntiHIV-Pred: Web-resource for in silico prediction of anti-HIV/AIDS activity.

Author information

1
Institute of Biomedical Chemistry, 10/8 Pogodinskaya Str., Moscow, Russia.
2
Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, Maryland, United States.

Abstract

MOTIVATION:

Identification of new molecules promising for treatment of HIV-infection and HIV-associated disorders remains an important task in order to provide safer and more effective therapies. Utilization of prior knowledge by application of computer-aided drug discovery approaches reduces time & financial expenses and increases the chances of positive results in anti-HIV R&D. To provide the scientific community with a tool that allows estimating of potential agents for treatment of HIV-infection and its comorbidities, we have created a freely-available web-resource for prediction of relevant biological activities based on the structural formulae of drug-like molecules.

RESULTS:

Over 50,000 experimental records for antiretroviral agents from ChEMBL database were extracted for creating the training sets. After careful examination, about seven thousand molecules inhibiting five HIV-1 proteins were used to develop regression and classification models with the GUSAR software. The average values of R2=0.95 and Q2=0.72 in validation procedure demonstrated the reasonable accuracy and predictivity of the obtained (Q)SAR models. Prediction of 81 biological activities associated with the treatment of HIV-associated comorbidities with 92% mean accuracy was realized using the PASS program.

AVAILABILITY:

Freely available on the web at http://www.way2drug.com/hiv/.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

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