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Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity
http://hdl.handle.net/10061/0002000435
http://hdl.handle.net/10061/000200043505ef080b-e8c2-464e-a85b-bc0123a98006
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||
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| 公開日 | 2024-05-28 | |||||||||
| タイトル | ||||||||||
| タイトル | Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | Activity cliff | |||||||||
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| 主題Scheme | UDC | |||||||||
| 主題 | Machine learning | |||||||||
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| 主題Scheme | Other | |||||||||
| 主題 | Deep learning | |||||||||
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| 主題Scheme | Other | |||||||||
| 主題 | Compound pair-based prediction | |||||||||
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| 主題Scheme | Other | |||||||||
| 主題 | Large-scale analysis | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ | journal article | |||||||||
| アクセス権 | ||||||||||
| アクセス権 | open access | |||||||||
| 著者 |
Tamura, Shunsuke
× Tamura, Shunsuke
× 宮尾, 知幸× Bajorath, J$00FCrgen
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| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | Activity cliffs (AC) are formed by pairs of structural analogues that are active against the same target but have a large difference in potency. While much of our knowledge about ACs has originated from the analysis and comparison of compounds and activity data, several studies have reported AC predictions over the past decade. Different from typical compound classification tasks, AC predictions must be carried out at the level of compound pairs representing ACs or nonACs. Most AC predictions reported so far have focused on individual methods or comparisons of two or three approaches and only investigated a few compound activity classes (from 2 to 10). Although promising prediction accuracy has been reported in most cases, different system set-ups, AC definitions, methods, and calculation conditions were used, precluding direct comparisons of these studies. Therefore, we have carried out a large-scale AC prediction campaign across 100 activity classes comparing machine learning methods of greatly varying complexity, ranging from pair-based nearest neighbor classifiers and decision tree or kernel methods to deep neural networks. The results of our systematic predictions revealed the level of accuracy that can be expected for AC predictions across many different compound classes. In addition, prediction accuracy did not scale with methodological complexity but was significantly influenced by memorization of compounds shared by different ACs or nonACs. In many instances, limited training data were sufficient for building accurate models using different methods and there was no detectable advantage of deep learning over simpler approaches for AC prediction. On a global scale, support vector machine models performed best, by only small margins compared to others including simple nearest neighbor classifiers. | |||||||||
| 書誌情報 |
en : Journal of Cheminformatics 巻 15, 号 1, 発行日 2023-01-07 |
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| 出版者 | ||||||||||
| 出版者 | BioMed Central | |||||||||
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| 収録物識別子タイプ | EISSN | |||||||||
| 収録物識別子 | 1758-2946 | |||||||||
| 出版者版DOI | ||||||||||
| 関連タイプ | isReplacedBy | |||||||||
| 識別子タイプ | DOI | |||||||||
| 関連識別子 | https://doi.org/10.1186/s13321-022-00676-7 | |||||||||
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| 関連タイプ | isReplacedBy | |||||||||
| 識別子タイプ | URI | |||||||||
| 関連識別子 | https://jcheminf.biomedcentral.com/articles/10.1186/s13321-022-00676-7 | |||||||||
| 権利 | ||||||||||
| 権利情報Resource | http://creativecommons.org/licenses/by/4.0/ | |||||||||
| 権利情報 | $00A9 The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. | |||||||||
| 著者版フラグ | ||||||||||
| 出版タイプ | NA | |||||||||
