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Journal of Computer-Aided Molecular Design

, Volume 30, Issue 9, pp 761–771 | Cite as

A D3R prospective evaluation of machine learning for protein-ligand scoring

  • Jocelyn Sunseri
  • Matthew Ragoza
  • Jasmine Collins
  • David Ryan KoesEmail author
Article

Abstract

We assess the performance of several machine learning-based scoring methods at protein-ligand pose prediction, virtual screening, and binding affinity prediction. The methods and the manner in which they were trained make them sufficiently diverse to evaluate the utility of various strategies for training set curation and binding pose generation, but they share a novel approach to classification in the context of protein-ligand scoring. Rather than explicitly using structural data such as affinity values or information extracted from crystal binding poses for training, we instead exploit the abundance of data available from high-throughput screening to approach the problem as one of discriminating binders from non-binders. We evaluate the performance of our various scoring methods in the 2015 D3R Grand Challenge and find that although the merits of some features of our approach remain inconclusive, our scoring methods performed comparably to a state-of-the-art scoring function that was fit to binding affinity data.

Keywords

Protein-ligand scoring Machine learning Virtual screening D3R 

Notes

Acknowledgments

We thank the organizers of D3R for their time and effort in running this invaluable exercise. We also are grateful for Nick Rego for his code for calculating SASA protein-ligand interaction terms.

Compliance with ethical standards

Funding

National Institute of General Medical Sciences [R01GM108340].

Supplementary material

10822_2016_9960_MOESM1_ESM.pdf (1.1 mb)
Supplementary material 1 (PDF 1107 kb)

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computational and Systems Biology, School of MedicineUniversity of PittsburghPittsburghUSA

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