Send to

Choose Destination
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

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



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.


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.


Freely available on the web at


Supplementary data are available at Bioinformatics online.

Supplemental Content

Full text links

Icon for Silverchair Information Systems
Loading ...
Support Center