Woong‐Hee Shin

MOZAIC generates chemically diverse drug-like molecules by combining fragment-based growth with global optimization, improving lead-like properties such as QED and synthetic accessibility.

This paper benchmarks sequence-based and AlphaFold-based methods for predicting pMHC-II binding cores, finding that AlphaFold-based methods excel at identifying binders while NetMHCIIpan is better at identifying non-binders.

KASSPer predicts kinase active-site conformations using protein and ligand language models to improve virtual screening efficiency and performance.

PL-PatchSurfer3 improves structure-based virtual screening by incorporating hydrogen bond complementarity and visibility features, enhancing performance while maintaining tolerance to receptor structure changes.

CoBRA predicts RNA ligand-binding sites using sequence-based embeddings from RNA language models, outperforming structure-based methods without requiring structural data.

This study deimmunizes a Salmonella phosphatase by mutating buried residues, enhancing its catalytic function while reducing immunogenicity.

This paper introduces a multi-state modeling protocol for kinases using AlphaFold2 to improve structure-based virtual screening by overcoming structural biases and enhancing the identification of diverse inhibitors.

The paper identifies PP-2, a src-kinase inhibitor, as a novel corrector for ΔF508-CFTR in cystic fibrosis through in silico screening and experimental validation.