Deep Learning-Driven Protein-Ligand Binding Affinity Prediction: Data, Architecture, Training and Evaluation

Prediction of protein-ligand binding affinity (PLA) is a crucial problem in drug discovery. Recently, deep learning (DL) models have emerged as a promising and computationally efficient paradigm for the PLA prediction task, enabling rapid and scalable analysis while circumventing the time-consuming nature of experimental assays and the rigidity of conventional scoring functions. However, a significant domain knowledge gap often prohibits the effective integration of biological and computational insights, making it challenging to design deep learning models that comprehensively capture all relevant aspects. Training such models remains a complex undertaking involving multiple facets, including data heterogeneity, model interpretability, and biological plausibility. This review explores the key considerations for training DL models in PLA prediction task, including the choice of datasets, data processing techniques, model architecture design, model training strategies, and evaluation methodologies. Additionally, we discuss the potential applications of PLA prediction in traditional drug discovery and emerging areas, along with the challenges that currently hinder the optimal utilization of deep learning models in this field. This review aims to bridge the gap between computational biology and deep learning by providing a comprehensive guide for researchers interested in leveraging deep learning for PLA prediction.