Vladimir Poroikov

The paper identifies small molecules targeting AML by screening for compounds with predicted functions in apoptosis, glutathione metabolism, and autophagy, validating their efficacy in AML cells and patient-derived samples.

This paper develops a method using generative topographic mapping to predict HIV drug resistance from genomic data, achieving high accuracy and interpretability.

The paper identifies common immune dysfunction mechanisms across nine viral infections by analyzing transcription profiles from PBMCs and highlights novel receptor associations with immune disruption.

This study identifies clarithromycin as a potential therapeutic for inflammatory bowel disease through in-silico drug repositioning and experimental validation of its effects on NF-κB signaling.

The paper develops a computational approach to assess cardiovascular adverse effects of drug-drug interactions using spontaneous reports and predicted drug-target interactions, identifying dangerous drug combinations.