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.
|Journal||Wiley Interdisciplinary Reviews: Computational Molecular Science|
|Early online date||3 Aug 2021|
|Publication status||Published - Mar 2022|
Scopus Subject Areas
- Computer Science Applications
- Physical and Theoretical Chemistry
- Computational Mathematics
- Materials Chemistry
- artificial intelligence
- feature engineering
- machine learning
- protein–ligand interaction
- scoring functions