Artem R Cherkasov

The paper uses machine learning-augmented virtual screening to discover a novel series of Mac1 inhibitors for SARS-CoV-2 NSP3, validated by experimental assays and crystallography.

This study designs potent anti-biofilm antimicrobial peptides using an explainable AI framework, achieving high efficacy against MRSA biofilms.

This study uses deep modeling to predict gain-of-function mutations in the androgen receptor and their drug responses, achieving 80% accuracy in identifying agonist/antagonist relationships.

This paper identifies m-APTA, an MTA analogue that reduces NBA toxicity without hypothermia, improving safety for MTAP-deficient cancer treatment.

This study identifies M19-6 as a small molecule that disrupts the eIF4F complex, reducing melanoma growth and metastasis while sparing melanocytes.

The paper evaluates computational hit-finding methods for targeting the WDR domain of LRRK2, a Parkinson's disease protein, finding that diverse strategies yielded weakly potent compounds.

PROTACable automates PROTAC design using 3-D modeling and deep learning to predict activity of newly designed PROTACs.

The paper identifies small molecules that enhance stem cell differentiation into definitive endoderm, reducing costs by 90% through a drug repurposing approach.

The paper develops a deep learning model to predict ligand-target interactions using protein structure graphs and attention mechanisms, achieving state-of-the-art performance on benchmark datasets.

The paper introduces Deep Docking (D2), a deep learning method for virtual screening of large chemical datasets, achieving significant data reduction and enrichment of top hits.