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 http://www.way2drug.com/hiv/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Identification of rodent carcinogens is an important task in risk assessment of chemicals. SAR methods were proposed to reduce the number of animal experiments. Most of these methods ignore information about organ-specificity of tumorigenesis. Our study was aimed at the creation of classification models and a freely available online service for prediction of rodent carcinogens considering the species (rats, mice), sex and tissue-specificity from structural formula of compounds. Results: The data from Carcinogenic Potency Database for 1011 organic compounds evaluated on the standard two-year rodent carcinogenicity bioassay was used for the creation of training sets. Structure-activity relationships models for prediction of rodent organ-specific carcinogenicity were created by PASS software, which was based on Bayesian-like approach and Multilevel Neighborhoods of Atoms descriptors. The average prediction accuracy for training sets calculated by leave-one-out and 10-fold cross-validation was 79 and 78.2%, respectively. Availability and implementation: Freely available on the web at http://www.way2drug.com/ROSC. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.