The algebraic extended atom-type graph-based model for precise ligand–receptor binding affinity prediction

Abstract

Accurate prediction of ligand-receptor binding affinity is crucial in structure-based drug design, significantly impacting the development of effective drugs. Recent advances in machine learning (ML)-based scoring functions have improved these predictions, yet challenges remain in modeling complex molecular interactions. This study introduces the AGL-EAT-Score, a scoring function that integrates extended atom-type multiscale weighted colored subgraphs with algebraic graph theory. This approach leverages the eigenvalues and eigenvectors of graph Laplacian and adjacency matrices to capture high-level details of specific atom pairwise interactions. Evaluated against benchmark datasets such as CASF-2016, CASF-2013, and the Cathepsin S dataset, the AGL-EAT-Score demonstrates notable accuracy, outperforming existing traditional and ML-based methods. The model’s strength lies in its comprehensive similarity analysis, examining protein sequence, ligand structure, and binding site similarities, thus ensuring minimal bias and over-representation in the training sets. The use of extended atom types in graph coloring enhances the model’s capability to capture the intricacies of protein-ligand interactions. The AGL-EAT-Score marks a significant advancement in drug design, offering a tool that could potentially refine and accelerate the drug discovery process.

Farjana Mukta

Lecturer of Mathematics, Kennesaw State University (Former Nguyen Lab PhD Student)

Farjana Tasnim Mukta is a Lecturer of Mathematics at Kennesaw State University. She received her PhD in Mathematics from the University of Kentucky in 2024, advised by Dr. Duc Nguyen. Her research focuses on advanced mathematical graph-based machine learning and deep learning models for drug design.

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.

Avery Meyer

Undergraduate Student (22-23)

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.