Machine learning and ligand binding predictions: A review of data, methods, and obstacles

• Published in: Biochimica et biophysica acta. General subjects Vol. 1864; no. 6; p. 129545
• Main Authors: Ellingson, Sally R., Davis, Brian, Allen, Jonathan
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.06.2020; Elsevier
• Subjects: algorithms; artificial intelligence; BASIC BIOLOGICAL SCIENCES; data collection; Drug binding; Drug discovery; drugs; high-throughput screening methods; ligands; Machine learning; Overfitting; prediction; statistics; Drug discovery; Drug binding; Machine learning; Overfitting
• ISSN: 0304-4165; 1872-8006; 1872-8006
• DOI: 10.1016/j.bbagen.2020.129545

Computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data in exchange for time-intensive simulations. This paper reviews current trends in the use of machine learning for drug binding predictions, data sources to develop machine learning algorithms, and potential problems that may lead to overfitting and ungeneralizable models. A few popular datasets that can be used to develop virtual high-throughput screening models are characterized using spatial statistics to quantify potential biases. We can see from evaluating some common benchmarks that good performance correlates with models with high-predicted bias scores and models with low bias scores do not have much predictive power. A better understanding of the limits of available data sources and how to fix them will lead to more generalizable models that will lead to novel drug discovery. •Provides an overview of current trends in modeling drug binding predictions.•Potential biases in datasets that may inflate performance metrics can be quantified.•Popular benchmark drug binding datasets are characterized.