Chenglong Li

SMARTFlexDB links ligand-bound and ligand-free RNA structures to study conformational flexibility and small-molecule recognition, providing a resource for RNA-targeted drug discovery.

Pep2Mol generates 3D molecules targeting protein-protein interfaces using diffusion models, achieving state-of-the-art binding affinities for small-molecule inhibitors.

SMARTPocket uses geometric deep learning to predict RNA-small molecule binding pockets from 3D structures, outperforming existing methods and identifying cryptic ligandable sites.

GatorAffinity improves protein-ligand binding affinity prediction by combining synthetic structural data with experimental data, achieving better accuracy and generalizability.

SMARTBind uses deep learning and contrastive learning to identify small molecule binders to RNA without structural input, outperforming existing methods and reducing computational cost.

MGMG generates bioactivity-aware molecules using cell morphology and textual descriptions without target information, achieving comparable binding affinities to reference compounds.

This study develops a novel small molecule, LLL12B, which inhibits STAT3 signaling and enhances the efficacy of chemotherapy in ovarian cancer cells.