Systems and methods for drug design and discovery comprising applications of machine learning with differential geometric modeling

Abstract

Characteristics of molecules and/or biomolecular complexes may be predicted using differential geometry based methods in combination with trained machine learning models. Element specific and element interactive manifolds may be constructed using element interactive number density and/or element interactive charge density to represent the atoms or the charges in selected element sets. Feature data may include element interactive curvatures of various types derived from element specific and element interactive manifolds at various scales. Element interactive curvatures computed from various element interactive manifolds may be input to trained machine learning models, which may be derived from corresponding machine learning algorithms. These machine learning models may be trained to predict characteristics such as protein-protein or protein-ligand/protein/nucleic acid binding affinity, toxicity endpoints, free energy changes upon mutation, protein flexibility/rigidity/allosterism, membrane/globular protein mutation impacts, plasma protein binding, partition coefficient, permeability, clearance, and/or aqueous solubility, among others.

Duc Nguyen

Associate Professor of Mathematics

Duc Nguyen develops mathematical and AI frameworks for molecular bioscience, drug discovery, and scientific computing. His group blends differential geometry, graph theory, and machine learning to build high-fidelity models for biomolecular systems, with notable wins in the D3R Grand Challenges and collaborations with Pfizer and Bristol Myers Squibb. Supported by multiple NSF awards, he has advised students and postdocs across theory and applications of AI-driven drug design.