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

Sally R. Ellingson, Brian Davis, Jonathan Allen

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

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.

Original languageEnglish
Article number129545
JournalBiochimica et Biophysica Acta - General Subjects
Volume1864
Issue number6
DOIs
StatePublished - Jun 2020

Bibliographical note

Publisher Copyright:
© 2020 Elsevier B.V.

Keywords

  • Drug binding
  • Drug discovery
  • Machine learning
  • Overfitting

ASJC Scopus subject areas

  • Biophysics
  • Biochemistry
  • Molecular Biology

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