Leonid A. Stolbov

TL;DR: The current version of the PASS program for prediction of biological activity spectra of organic compounds based on analysis of structure—activity relationships (SAR) for a training set containing information on more than 1000 000 biologically active organic compounds is described.

TL;DR: This work has created a freely-available web-resource for prediction of relevant biological activities based on the structural formulae of drug-like molecules and Prediction of 81 biological activities associated with the treatment of HIV-associated comorbidities with 92% mean accuracy was realized using the PASS program.

TL;DR: The proposed combined computational approach, which combines structural similarity assessment, machine learning, and molecular modeling, has been validated in a project aimed at finding new pharmaceutical agents against HIV/AIDS and associated comorbidities from the Synthetically Accessible Virtual Inventory (SAVI).

TL;DR: A variety of (Q)SAR models are developed and validated, which can be further used for virtual screening of new antiretrovirals in the SAVI library, and implemented in the freely available web resource AntiHIV-Pred.

TL;DR: Pilot in vivo efficacy studies indicate anti-leukemic efficacy in a MOLM14/GFP/LUC xenograft model, including extended survival in mice injected with leukemic cells pre-treated with PS127B or PS127E and in mice treated with PS 127E at a dose of 5 mg/kg.