Jinjiang Guo

The paper benchmarks model scaling in AI-driven drug discovery, finding that compact models often outperform larger ones across various molecular property and activity prediction tasks.

DCBK predicts ligand dissociation kinetics by combining simulation and machine learning, achieving strong agreement with experiments and offering mechanistic insights.

This paper introduces GenVS-TBDB, an AI-generated and virtual-screened small-molecule library targeting Mycobacterium tuberculosis, identifying bioactive compounds with potential for tuberculosis drug discovery.

This paper introduces TamGen, a chemical language model for target-aware molecule generation, and shows it produces high-quality compounds with inhibitory activity against Tuberculosis ClpP protease.

This paper develops an in-silico drug repurposing pipeline for epilepsy using deep learning and structure-based methods, identifying potential drugs with high binding affinity for epilepsy-related targets.