A Survey of Graph Neural Network Architectures in Ligand Binding Affinity Prediction Models

Abstract

Ligand affinity prediction plays a pivotal role in drug discovery, influencing the efficiency and success of drug development processes. Traditional methods struggle in accurately capturing the complex interactions within molecular structures, prompting the exploration of advanced techniques such as Graph Neural Networks (GNNs). This paper provides an analysis of GNNs in the context of ligand affinity prediction, exploring their architecture, applications, and potential impact on revolutionizing drug discovery. Our findings suggest that GNNs can offer improvements over traditional computational methods, particularly in handling the dynamic and complex nature of molecular interactions. We highlight innovative GNN architectures that have shown notable success in predicting ligand binding affinities, such as heterogeneous graph representation and attention mechanisms. The implications of these advancements suggest a paradigm shift in drug discovery, where GNNs can lead to more accurate predictions and accelerate the identification of potential drug candidates. This study underscores the transformative potential of GNNs in enhancing predictive accuracy and efficiency in drug development.