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Tytuł:
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MILCDock: Machine Learning Enhanced Consensus Docking for Virtual Screening in Drug Discovery.
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Autorzy:
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Morris CJ; Department of Physics and Astronomy, Brigham Young University, Provo, Utah84602, United States.
Stern JA; Department of Physics and Astronomy, Brigham Young University, Provo, Utah84602, United States.; Department of Computer Science, Brigham Young University, Provo, Utah84602, United States.
Stark B; Department of Physics and Astronomy, Brigham Young University, Provo, Utah84602, United States.
Christopherson M; Department of Physics and Astronomy, Brigham Young University, Provo, Utah84602, United States.
Della Corte D; Department of Physics and Astronomy, Brigham Young University, Provo, Utah84602, United States.
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Źródło:
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Journal of chemical information and modeling [J Chem Inf Model] 2022 Nov 28; Vol. 62 (22), pp. 5342-5350. Date of Electronic Publication: 2022 Nov 07.
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Typ publikacji:
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Journal Article; Research Support, Non-U.S. Gov't
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Język:
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English
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Imprint Name(s):
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Original Publication: Washington, D.C. : American Chemical Society, c2005-
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MeSH Terms:
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Machine Learning*
Drug Discovery*
Molecular Docking Simulation ; Consensus ; Ligands ; Protein Binding
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Substance Nomenclature:
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0 (Ligands)
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Entry Date(s):
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Date Created: 20221107 Date Completed: 20221129 Latest Revision: 20221212
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Update Code:
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20240105
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DOI:
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10.1021/acs.jcim.2c00705
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PMID:
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36342217
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Molecular docking tools are regularly used to computationally identify new molecules in virtual screening for drug discovery. However, docking tools suffer from inaccurate scoring functions with widely varying performance on different proteins. To enable more accurate ranking of active over inactive ligands in virtual screening, we created a machine learning consensus docking tool, MILCDock, that uses predictions from five traditional molecular docking tools to predict the probability a ligand binds to a protein. MILCDock was trained and tested on data from both the DUD-E and LIT-PCBA docking datasets and shows improved performance over traditional molecular docking tools and other consensus docking methods on the DUD-E dataset. LIT-PCBA targets proved to be difficult for all methods tested. We also find that DUD-E data, although biased, can be effective in training machine learning tools if care is taken to avoid DUD-E's biases during training.