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Zheng S, Wang L, Xiong J, Liang G, Xu Y, Lin F. Consensus Prediction of Human Gut Microbiota-Mediated Metabolism Susceptibility for Small Molecules by Machine Learning, Structural Alerts, and Dietary Compounds-Based Average Similarity Methods. J Chem Inf Model 2022; 62:1078-1099. [PMID: 35156807 DOI: 10.1021/acs.jcim.1c00948] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
The human gut microbiota (HGM) colonizing human gastrointestinal tract (HGT) confers a repertoire of dynamic and unique metabolic capacities that are not possessed by the host and therefore is tentatively perceived as an alternative metabolic ″organ″ besides the liver in the host. Nevertheless, the significant contribution of HGM to the overall human metabolism is often overlooked in the modern drug discovery pipeline. Hence, a systematic evaluation of HGM-mediated drug metabolism is gradually important, and its computational prediction becomes increasingly necessary. In this work, a new data set containing both the HGM-mediated metabolism susceptible (HGMMS) and insusceptible (HGMMI) compounds (329 vs 320) was manually curated. Based on this data set, the first machine learning (ML) model, a new structural alerts (SA) model, and the K-nearest neighboring dietary compounds-based average similarity (AS) model were proposed to directly predict the HGM-mediated metabolism susceptibility for small molecules, and exhibit promising performance on three independent test sets. Finally, consensus prediction (ML/SA/AS) for DrugBank molecules revealed an intriguing phenomenon that a typical Michael acceptor ″α,β-unsaturated carbonyl group″ is a very common warhead for the design of covalent inhibitors and inclined to be metabolized by HGM in anaerobic HGT to generate the reduced metabolite without the reactive warhead, which could be a new concern to medicinal chemists. To the best of our knowledge, we gleaned the first HGMMS/HGMMI data set, developed the first HGMMS/HGMMI classification model, implemented a relatively comprehensive program based on ML/SA/AS approaches, and found a new phenomenon on the HGM-mediated deactivation of an extensively used warhead for covalent inhibitors.
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Affiliation(s)
- Suqing Zheng
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China.,Chemical Biology Research Center, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
| | - Lei Wang
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
| | - Jun Xiong
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
| | - Guang Liang
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China.,Chemical Biology Research Center, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
| | - Yong Xu
- Center of Chemical Biology, Guangzhou Institute of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, P.R. China
| | - Fu Lin
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
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Šícho M, de Bruyn Kops C, Stork C, Svozil D, Kirchmair J. FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity. J Chem Inf Model 2017; 57:1832-1846. [DOI: 10.1021/acs.jcim.7b00250] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Martin Šícho
- Faculty
of Mathematics, Informatics and Natural Sciences, Department of Computer
Science, Center for Bioinformatics, Universität Hamburg, Hamburg, 20146, Germany
- CZ-OPENSCREEN:
National Infrastructure for Chemical Biology, Laboratory of Informatics
and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, 166 28 Prague 6, Czech Republic
| | - Christina de Bruyn Kops
- Faculty
of Mathematics, Informatics and Natural Sciences, Department of Computer
Science, Center for Bioinformatics, Universität Hamburg, Hamburg, 20146, Germany
| | - Conrad Stork
- Faculty
of Mathematics, Informatics and Natural Sciences, Department of Computer
Science, Center for Bioinformatics, Universität Hamburg, Hamburg, 20146, Germany
| | - Daniel Svozil
- CZ-OPENSCREEN:
National Infrastructure for Chemical Biology, Laboratory of Informatics
and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, 166 28 Prague 6, Czech Republic
| | - Johannes Kirchmair
- Faculty
of Mathematics, Informatics and Natural Sciences, Department of Computer
Science, Center for Bioinformatics, Universität Hamburg, Hamburg, 20146, Germany
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