Enhancing the Performance of GNN and Utilizing 3D Instance Segmentation for Ligand Binding Site Prediction

Abstract

This study addresses the challenge of accurately predicting ligand binding sites (LBS) onproteins, a critical aspect of structure-based drug design. Ligand binding site prediction is crucial for designing effective drugs and understanding protein functions, benefiting pharmaceutical companies, biotechnologists, and researchers by accelerating drug discovery and improving therapeutic interventions. We employ and improve Graph Neural Networks (GNNs) and innovative 3D point cloud instance segmentation to refine and advance LBS prediction methods. This research demonstrates significant enhancements in predictive accuracy by evaluating these methods on widely used datasets. Our novel clustering algorithm, which combines density-based and fuzzy clustering, notably improves the definition and identification of ligand binding sites without prior knowledge of the number of clusters. This methodology allows for more precise predictions, effectively managing binding sites' overlapping nature. Implementing instance segmentation further delineates individual binding pockets, offering a more granular understanding of ligand-protein interactions. The results illustrate that our approaches meet the current state-of-the-art for ligand binding site prediction and support their potential utility in real- world pharmaceutical applications. Future work will focus on refining these methods and extending their application to molecular docking studies.