EISA-Score: Element Interactive Surface Area Score for Protein–Ligand Binding Affinity Prediction

Abstract

Molecular surface representations have been advertised as a great tool to study protein structure and functions, including protein–ligand binding affinity modeling. However, the conventional surface-area-based methods fail to deliver a competitive performance on the energy scoring tasks. The main reason is the lack of crucial physical and chemical interactions encoded in the molecular surface generations. We present novel molecular surface representations embedded in different scales of the element interactive manifolds featuring the dramatically dimensional reduction and accurately physical and biological properties encoders. Those low-dimensional surface-based descriptors are ready to be paired with any advanced machine learning algorithms to explore the essential structure–activity relationships that give rise to the element interactive surface area-based scoring functions (EISA-score). The newly developed EISA-score has outperformed many state-of-the-art models, including various well-established surface-related representations, in standard PDBbind benchmarks.

Masud Rana

Assistant Professor of Mathematics, Kennesaw State University (former Nguyen Lab postdoc)

Masud Rana is an assistant professor of mathematics at Kennesaw State University and a former postdoctoral scholar in the Nguyen Lab. He works on graph-theoretic and geometric methods for AI-driven drug discovery.

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.