Featurization strategies for protein-ligand interactions and their applications in scoring function development

The predictive performance of classical scoring functions (SFs) seems to have reached a plateau. Currently, SFs relying on sophisticated machine learning techniques have shown great potential in binding affinity prediction and virtual screening. As one of the most indispensable components in the workflow of training a machine learning scoring function (MLSF), the featurization or representation process enables us to catch certain physical processes that are important for protein-ligand interactions and to obtain machine-readable descriptors. Currently, according to how they are derived, the descriptors used in MLSFs for both continuous and binary binding affinity estimates can be grouped into two broad categories: handcrafted features and automated-extraction features. Moreover, the automated-extraction features emerge as a new featurization trend along with the application of deep learning algorithms. Here, we make a thorough summary of the advances in the featurization strategies for protein-ligand interactions in the context of MLSFs, with emphasis on the recently rising automated-extraction features. We also discuss the similarity between protein-ligand interaction representations and small-molecule representations, and the challenges confronted by the scientific community in characterizing protein-ligand interactions. We expect that this review could inspire the development of novel featurization approaches and boosted MLSFs. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Software > Molecular Modeling Molecular and Statistical Mechanics > Molecular Interactions

Automated summary

Classical scoring functions have reached a plateau in predictive performance, while machine learning scoring functions relying on sophisticated techniques show great potential in binding affinity prediction. Automated-extraction features are emerging as a new trend in featurization for protein-ligand interactions, helping capture important physical processes.