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Ligand-based Activity Cliff Prediction Models with Applicability Domain

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dc.contributor.author Tamura, Shunsuke en
dc.contributor.author Miyao, Tomoyuki en
dc.contributor.author Funatsu, Kimito en
dc.date.accessioned 2021-11-22T08:39:02Z en
dc.date.available 2021-11-22T08:39:02Z en
dc.date.issued 2020-12-01 en
dc.identifier.uri http://hdl.handle.net/10061/14551 en
dc.description.abstract Activity cliffs (ACs) are formed by pairs of structurally similar compounds with large differences in potency. Predicting ACs is of high interest in lead optimization for drug discovery. Previous AC prediction models that focused on matched molecular pair (MMP) cliffs produced adequate performances. However, the extrapolation ability of these models is unclear because the main scaffold for MMPs, the core structure, could exist in both training and test data sets. Also, representation of MMPs did not consider the attachment points where the core and R-group substituents are connected. In this study, we aimed to improve a ligand-based AC prediction method using molecular fingerprints. We incorporated applicability domain, which was defined using R-path fingerprints to consider the local environment around an attachment point. Rigorous evaluation of the extrapolation ability of AC prediction models showed that MMP-cliffs were accurately predicted for nine biological targets. Furthermore, incorporation of training MMPs with cores distinct from those of test MMPs improved the predictability compared with using training MMPs with only similar cores. en
dc.language.iso en en
dc.publisher Wiley-VCH Verlag en
dc.relation.isversionof https://onlinelibrary.wiley.com/doi/full/10.1002/minf.202000103 en
dc.rights © 2020 Wiley-VCH GmbH This is the peer reviewed version of the following article: [https://onlinelibrary.wiley.com/doi/full/10.1002/minf.202000103], which has been published in final form at [https://doi.org/10.1002/minf.202000103]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited. ja
dc.subject activity cliff prediction en
dc.subject applicability domain en
dc.subject drug design en
dc.subject ligand-based approach en
dc.subject structure-activity relationships en
dc.title Ligand-based Activity Cliff Prediction Models with Applicability Domain 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 author en
dc.identifier.eissn 1868-1751 en
dc.identifier.jtitle Molecular Informatics en
dc.identifier.volume 39 en
dc.identifier.issue 12 en
dc.relation.doi 10.1002/minf.202000103 en
dc.identifier.NAIST-ID 86632445 en
dc.identifier.NAIST-ID 74654633 en
dc.identifier.NAIST-ID 74654427 en
dc.relation.pmid 32830451 en

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