Leonid Stolbov1, Dmitry Druzhilovskiy1, Anastasia Rudik1, Dmitry Filimonov1, Vladimir Poroikov1, Marc Nicklaus2. 1. Laboratory for Structure-Function Based Drug Design, Institute of Biomedical Chemistry, Moscow 119121, Russia. 2. CADD Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, NCI-Frederick, Frederick, MD 21702, USA.
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 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 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.
Authors: Leonid A Stolbov; Dmitry S Druzhilovskiy; Dmitry A Filimonov; Marc C Nicklaus; Vladimir V Poroikov Journal: Molecules Date: 2019-12-25 Impact factor: 4.411