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

Bioinformatics. 2020 Feb 1;36(3):978-979. doi: 10.1093/bioinformatics/btz638.


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 and 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 anti-retroviral 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 and implementation: Freely available on the web at

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computer Simulation
  • HIV Infections* / drug therapy
  • HIV* / genetics
  • Prednisolone* / analogs & derivatives
  • Proteins
  • Software*
  • Structure-Activity Relationship
  • Viral Proteins*


  • Proteins
  • Viral Proteins
  • Prednisolone
  • prednylidene