Hao Fan

This paper compares ligand-based and hybrid machine learning models for predicting CYP450 ligand binding, finding the hybrid model outperforms existing methods in accuracy.

This study uses computational analysis to explore how nucleotide states and ligand binding in P-glycoprotein influence drug transport and allosteric signaling.

This study identifies Mitoxantrone hydrochloride as a drug that targets APP and LRRK2 to improve neurodegeneration in Parkinson's disease models.

This study improves virtual screening of AChE inhibitors by combining multi-structure covalent docking with a machine learning consensus model, achieving higher predictive accuracy than single-structure approaches.

This study identifies a new IMiD-independent drug targeting SALL4's ZFC4 domain, leading to significant tumor growth inhibition in vivo.

This study compares X-ray and NMR protein structures in virtual ligand screening, finding X-ray structures generally perform better in ligand enrichment and binding-site configuration.

This paper develops a molecular dynamics protocol to predict EGFR mutation effects and applies it to three rare mutations, showing how they alter structural dynamics and activity.

This study uses computational methods to investigate how nucleotide asymmetry and linker dynamics influence the drug efflux mechanism of P-glycoprotein.

This study identifies non-Cannabis BBE superfamily enzymes that catalyze CBGA to CBSA, opening new avenues for cannabinoid biosynthesis.

This study identifies four FDA-approved drugs as novel non-peptide allosteric inhibitors of caspase-3/7 through virtual screening and experimental validation.

This study benchmarks homology models of human GPCRs for virtual ligand screening, finding that 10 out of 19 GPCRs show comparable performance to crystal structures, with consensus methods improving results.