Protein-Ligand Docking in the Machine-Learning Era

Molecular docking plays a significant role in early-stage drug discovery, from structure-based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive power is critically dependent on the protein-ligand scoring function. In this review, we give a broad overview of recent scoring function development, as well as the docking-based applications in drug discovery. We outline the strategies and resources available for structure-based VS and discuss the assessment and development of classical and machine learning protein-ligand scoring functions. In particular, we highlight the recent progress of machine learning scoring function ranging from descriptor-based models to deep learning approaches. We also discuss the general workflow and docking protocols of structure-based VS, such as structure preparation, binding site detection, docking strategies, and post-docking filter/re-scoring, as well as a case study on the large-scale docking-based VS test on the LIT-PCBA data set.

Automated summary

Molecular docking plays a significant role in early-stage drug discovery, and its success relies on the protein-ligand scoring function. This review provides an overview of recent scoring function development and docking-based applications in drug discovery. It discusses the strategies and resources for structure-based virtual screening, as well as the evaluation and development of classical and machine learning protein-ligand scoring functions. The review highlights the recent progress in machine learning scoring functions, including descriptor-based models and deep learning approaches. It also discusses the general workflow and docking protocols of structure-based virtual screening, along with a case study on large-scale docking-based virtual screening.