Recent advances in AI-driven protein-ligand interaction predictions

Abstract

Structure-based drug discovery is a fundamental approach in modern drug development, leveraging computational models to predict protein-ligand interactions. AI-driven methodologies are significantly improving key aspects of the field, including ligand binding site prediction, protein-ligand binding pose estimation, scoring function development, and virtual screening. In this review, we summarize the recent AI-driven advances in various protein-ligand interaction prediction tasks. Traditional docking methods based on empirical scoring functions often lack accuracy, whereas AI models, including graph neural networks, mixture density networks, transformers, and diffusion models, have enhanced predictive performance. Ligand binding site prediction has been refined using geometric deep learning and sequence-based embeddings, aiding in the identification of potential druggable target sites. Binding pose prediction has evolved with sampling-based and regression-based models, as well as protein-ligand co-generation frameworks. AI-powered scoring functions now integrate physical constraints and deep learning techniques to improve binding affinity estimation, leading to more robust virtual screening strategies. Despite these advances, generalization across diverse protein-ligand pairs remains a challenge. As AI technologies continue to evolve, they are expected to revolutionize molecular docking and affinity prediction, increasing both the accuracy and efficiency of structure-based drug discovery. • AI-driven methods enhance protein-ligand interaction predictions across pose prediction, scoring, and virtual screening. • Diffusion and geometric deep learning models improve ligand pose prediction and scoring functions. • Hybrid approaches integrating sequence and structure-based embeddings refine ligand binding site identification. • AI-based scoring functions enhance virtual screening accuracy, surpassing traditional docking methods. • Incorporating protein flexibility and diverse data can enhance AI-driven drug discovery, addressing generalizability challenges.

Topics & Concepts