Modern machine‐learning for binding affinity estimation of protein–ligand complexes: Progress, opportunities, and challenges

Abstract Structure‐based drug design is a widely applied approach in the discovery of new lead compounds for known therapeutic targets. In most structure‐based drug design applications, the docking procedure is considered the crucial step. Here, a potential ligand is fitted into the binding site, and a scoring function assesses its binding capability. With the rise of modern machine‐learning in drug discovery, novel scoring functions using machine‐learning techniques achieved significant performance gains in virtual screening and ligand optimization tasks on retrospective data. However, real‐world applications of these methods are still limited. Missing success stories in prospective applications are one reason for this. Additionally, the fast‐evolving nature of the field makes it challenging to assess the advantages of each individual method. This review will highlight recent strides toward improved real world applicability of machine‐learning based scoring, enabling a better understanding of the potential benefits and pitfalls of these functions on a project. Furthermore, a systematic way of classifying machine‐learning based scoring that facilitates comparisons will be presented. This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning Software > Molecular Modeling