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.

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