Fuqiang Ban

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

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