Interaction-Based Inductive Bias in Graph Neural Networks: Enhancing Protein-Ligand Binding Affinity Predictions From 3D Structures

• Published in: IEEE transactions on pattern analysis and machine intelligence 2024-12, Vol.46 (12), p.8191-8208
• Main Authors: Yang, Ziduo, Zhong, Weihe, Lv, Qiujie, Dong, Tiejun, Chen, Guanxing, Chen, Calvin Yu-Chian
• Format: Article
• Language: English
• Subjects: Algorithms; Convolution; COVID-19; Data models; drug-target interaction; Graph neural networks; Humans; inductive bias; Ligands; Machine Learning; Neural Networks, Computer; Predictive models; Programmable logic arrays; Protein Binding; Protein Conformation; Protein-ligand binding affinity; Proteins; Proteins - chemistry; Proteins - metabolism; SARS-CoV-2; structure-based virtual screening; Three-dimensional displays
• ISSN: 0162-8828; 1939-3539; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2024.3400515

Inductive bias in machine learning (ML) is the set of assumptions describing how a model makes predictions. Different ML-based methods for protein-ligand binding affinity (PLA) prediction have different inductive biases, leading to different levels of generalization capability and interpretability. Intuitively, the inductive bias of an ML-based model for PLA prediction should fit in with biological mechanisms relevant for binding to achieve good predictions with meaningful reasons. To this end, we propose an interaction-based inductive bias to restrict neural networks to functions relevant for binding with two assumptions: 1) A protein-ligand complex can be naturally expressed as a heterogeneous graph with covalent and non-covalent interactions; 2) The predicted PLA is the sum of pairwise atom-atom affinities determined by non-covalent interactions. The interaction-based inductive bias is embodied by an explainable heterogeneous interaction graph neural network (EHIGN) for explicitly modeling pairwise atom-atom interactions to predict PLA from 3D structures. Extensive experiments demonstrate that EHIGN achieves better generalization capability than other state-of-the-art ML-based baselines in PLA prediction and structure-based virtual screening. More importantly, comprehensive analyses of distance-affinity, pose-affinity, and substructure-affinity relations suggest that the interaction-based inductive bias can guide the model to learn atomic interactions that are consistent with physical reality. As a case study to demonstrate practical usefulness, our method is tested for predicting the efficacy of Nirmatrelvir against SARS-CoV-2 variants. EHIGN successfully recognizes the changes in the efficacy of Nirmatrelvir for different SARS-CoV-2 variants with meaningful reasons.