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Molecules 2015, 20(6), 10947-10962; doi:10.3390/molecules200610947

Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest

1
Department of Computer Science and Engineering, Chinese University of Hong Kong, Sha Tin, New Territories 999077, Hong Kong
2
Cancer Research Center of Marseille, INSERM U1068, F-13009 Marseille, France
*
Author to whom correspondence should be addressed.
Academic Editor: Peter Willett
Received: 13 March 2015 / Revised: 4 June 2015 / Accepted: 9 June 2015 / Published: 12 June 2015
(This article belongs to the Special Issue Chemoinformatics)
View Full-Text   |   Download PDF [962 KB, uploaded 12 June 2015]   |  

Abstract

Docking scoring functions can be used to predict the strength of protein-ligand binding. It is widely believed that training a scoring function with low-quality data is detrimental for its predictive performance. Nevertheless, there is a surprising lack of systematic validation experiments in support of this hypothesis. In this study, we investigated to which extent training a scoring function with data containing low-quality structural and binding data is detrimental for predictive performance. We actually found that low-quality data is not only non-detrimental, but beneficial for the predictive performance of machine-learning scoring functions, though the improvement is less important than that coming from high-quality data. Furthermore, we observed that classical scoring functions are not able to effectively exploit data beyond an early threshold, regardless of its quality. This demonstrates that exploiting a larger data volume is more important for the performance of machine-learning scoring functions than restricting to a smaller set of higher data quality. View Full-Text
Keywords: docking; binding affinity prediction; machine-learning scoring functions docking; binding affinity prediction; machine-learning scoring functions
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, H.; Leung, K.-S.; Wong, M.-H.; Ballester, P.J. Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest. Molecules 2015, 20, 10947-10962.

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