Graph and Convolutional Methods for Advancing Ligand Binding Affinity Modeling in Drug Research

Drug discovery increasingly relies on accurate ligand binding affinity modeling to reduce the cost and time spent on trial-and-error experiments. However, existing computational methods often exhibit limited generalization, interpretability, and training efficiency. To address these gaps, we present a novel framework that integrates graph neural networks (GNNs) and convolutional models to model proteinligand interactions. Our approach builds on recent findings that highlight the benefits of representing protein-ligand complexes through graph topologies while capturing spatial and structural features using convolutional layers. We examine two publicly available datasets, PDBbind and BindingDB, both of which exemplify diverse protein-ligand complexes. Comprehensive experiments demonstrate that our integrated GNN-convolutional model improves predictive accuracy, reduces computational costs, and enhances interpretability. In addition, ablation studies reveal the roles of graph structural encoding and convolutional feature extraction in capturing crucial interaction signals. Theoretically, our study augments existing research by providing empirical evidence that unifying graph and convolutional strategies can enrich the insight into topological and spatial representation learning in ligand binding affinity prediction. Practically, the proposed framework can be readily adopted in workflows where large-scale exploration of protein-ligand complexes is required, potentially accelerating early-stage drug discovery by refining virtual screening and lead optimization. This work closes previously identified performance and interpretability gaps, offering a rigorous pathway to future applications in ligand binding affinity modeling.