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Comparison of Data Fusion Methods as Consensus Scores for Ensemble Docking
Article

Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1

1
INSERM U1133, CNRS UMR 8251, Unit of functional and adaptive biology, Université de Paris, Paris 75013, France
2
Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
3
Institute of Biological Psychiatry, Mental Health Centre Sct. Hans, Copenhagen University Hospital, 4000 Roskilde, Denmark
4
Department of Science and Environment, Roskilde University, 4000 Roskilde, Denmark
5
Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
*
Author to whom correspondence should be addressed.
Academic Editor: Dragos Horvath
Molecules 2019, 24(15), 2747; https://doi.org/10.3390/molecules24152747
Received: 10 June 2019 / Revised: 11 July 2019 / Accepted: 24 July 2019 / Published: 29 July 2019
(This article belongs to the Special Issue Molecular Docking in Drug Design 2018)
The human carboxylesterase 1 (CES1), responsible for the biotransformation of many diverse therapeutic agents, may contribute to the occurrence of adverse drug reactions and therapeutic failure through drug interactions. The present study is designed to address the issue of potential drug interactions resulting from the inhibition of CES1. Based on an ensemble of 10 crystal structures complexed with different ligands and a set of 294 known CES1 ligands, we used docking (Autodock Vina) and machine learning methodologies (LDA, QDA and multilayer perceptron), considering the different energy terms from the scoring function to assess the best combination to enable the identification of CES1 inhibitors. The protocol was then applied on a library of 1114 FDA-approved drugs and eight drugs were selected for in vitro CES1 inhibition. An inhibition effect was observed for diltiazem (IC50 = 13.9 µM). Three others drugs (benztropine, iloprost and treprostinil), exhibited a weak CES1 inhibitory effects with IC50 values of 298.2 µM, 366.8 µM and 391.6 µM respectively. In conclusion, the binding site of CES1 is relatively flexible and can adapt its conformation to different types of ligands. Combining ensemble docking and machine learning approaches improves the prediction of CES1 inhibitors compared to a docking study using only one crystal structure. View Full-Text
Keywords: carboxylesterase 1; docking; ensemble docking; machine learning; CES1 inhibitors; adverse drug reactions; metabolism carboxylesterase 1; docking; ensemble docking; machine learning; CES1 inhibitors; adverse drug reactions; metabolism
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MDPI and ACS Style

Briand, E.; Thomsen, R.; Linnet, K.; Rasmussen, H.B.; Brunak, S.; Taboureau, O. Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1. Molecules 2019, 24, 2747. https://doi.org/10.3390/molecules24152747

AMA Style

Briand E, Thomsen R, Linnet K, Rasmussen HB, Brunak S, Taboureau O. Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1. Molecules. 2019; 24(15):2747. https://doi.org/10.3390/molecules24152747

Chicago/Turabian Style

Briand, Eliane, Ragnar Thomsen, Kristian Linnet, Henrik B. Rasmussen, Søren Brunak, and Olivier Taboureau. 2019. "Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1" Molecules 24, no. 15: 2747. https://doi.org/10.3390/molecules24152747

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