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Interpretation of Ligand-Based Activity Cliff Prediction Models Using the Matched Molecular Pair Kernel

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dc.contributor.author Tamura, Shunsuke en
dc.contributor.author Jasial, Swarit en
dc.contributor.author Miyao, Tomoyuki en
dc.contributor.author Funatsu, Kimito en
dc.date.accessioned 2021-10-25T10:34:42Z en
dc.date.available 2021-10-25T10:34:42Z en
dc.date.issued 2021-08-13 en
dc.identifier.uri http://hdl.handle.net/10061/14509 en
dc.description.abstract Activity cliffs (ACs) are formed by two structurally similar compounds with a large difference in potency. Accurate AC prediction is expected to help researchers’ decisions in the early stages of drug discovery. Previously, predictive models based on matched molecular pair (MMP) cliffs have been proposed. However, the proposed methods face a challenge of interpretability due to the black-box character of the predictive models. In this study, we developed interpretable MMP fingerprints and modified a model-specific interpretation approach for models based on a support vector machine (SVM) and MMP kernel. We compared important features highlighted by this SVM-based interpretation approach and the SHapley Additive exPlanations (SHAP) as a major model-independent approach. The model-specific approach could capture the difference between AC and non-AC, while SHAP assigned high weights to the features not present in the test instances. For specific MMPs, the feature weights mapped by the SVM-based interpretation method were in agreement with the previously confirmed binding knowledge from X-ray co-crystal structures, indicating that this method is able to interpret the AC prediction model in a chemically intuitive manner. ja
dc.language.iso en en
dc.publisher MDPI en
dc.relation.isreplacedby https://www.mdpi.com/1420-3049/26/16/4916 en
dc.rights © 2021 by the authors. Licensee MDPI, Basel, Switzerland.This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). ja
dc.subject chemoinformatics en
dc.subject activity-cliff en
dc.subject support vector machine en
dc.subject model interpretation en
dc.subject SHapley Additive exPlanations en
dc.subject matched molecular pair en
dc.title Interpretation of Ligand-Based Activity Cliff Prediction Models Using the Matched Molecular Pair Kernel en
dc.type.nii Journal Article en
dc.contributor.transcription タムラ, シュンスケ ja
dc.contributor.transcription ミヤオ, トモユキ ja
dc.contributor.transcription フナツ, キミト ja
dc.contributor.alternative 田村, 峻佑 ja
dc.contributor.alternative 宮尾, 知幸 ja
dc.contributor.alternative 船津, 公人 ja
dc.textversion none en
dc.identifier.eissn 1420-3049 en
dc.identifier.jtitle Molecules en
dc.identifier.volume 26 en
dc.identifier.issue 16 en
dc.relation.doi 10.3390/molecules26164916 en
dc.identifier.NAIST-ID 86632445 en
dc.identifier.NAIST-ID 74655846 en
dc.identifier.NAIST-ID 74654633 en
dc.identifier.NAIST-ID 74654427 en
dc.relation.pmid 34443503 en

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