Shu-Hang Lin

PHENOMS analyzes hydrogen-bond networks in molecular dynamics simulations using Rust-accelerated code, achieving significant speedup over Python-based tools and demonstrating its utility with HDX-MS data.

This study reveals the molecular mechanism of nirmatrelvir binding to SARS-CoV-2 main protease using combined NMR titrations and weighted ensemble simulations, identifying two distinct binding pathways and key residues involved in resistance.

The paper evaluates ML and physics-based methods for predicting protein-ligand binding using DNA-encoded libraries, finding that no single method consistently generalizes to out-of-distribution chemical space.

DELi is an open-source platform for DNA-encoded library design, decoding, and analysis, demonstrating its effectiveness through the identification of a BRD4 binder.

This paper combines DNA-encoded libraries with generative AI to expand hit compounds, finding AI-selected hits show better drug-likeness and diversity than initial DEL hits.

This study uses AlphaFold2 Steering and machine learning to model kinase conformations and identify state-specific inhibitors, achieving a 53% hit rate in virtual screening and validating conformation-specificity for FLT3 ligands.