Ruibin Liu

This paper introduces AiPP, an AI-based platform for predicting ligandable sites in the human proteome, and demonstrates its application in discovering covalent inhibitors for PTPN6.

This paper develops KaML models using decision trees and graph attention networks to predict protein pKa values and ionization states, outperforming existing methods and introducing a new pKa database.

This study evaluates force field limitations in constant pH molecular dynamics simulations of a mini-protein, showing significant pKa errors due to force field and water model choices.

The paper develops machine learning models to classify intrinsic activities of ligands at the human mu-opioid receptor using molecular descriptors and SMILES strings, achieving high accuracy even with limited data.

This study investigates why the Omicron variant's main protease is less stable than the wild-type by analyzing its structure, enzymatic activity, and biophysical properties.

The paper develops ML models to predict covalent ligandable cysteines using a curated database, achieving high accuracy and providing a web tool for drug discovery.