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Generalizability Improvement of Interpretable Symbolic Regression Models for Quantitative Structure$2013Activity Relationships
http://hdl.handle.net/10061/0002000731
http://hdl.handle.net/10061/00020007313cdeac49-6b19-4953-83ef-5d6675aed844
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||
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| 公開日 | 2024-12-27 | |||||||||
| タイトル | ||||||||||
| タイトル | Generalizability Improvement of Interpretable Symbolic Regression Models for Quantitative Structure$2013Activity Relationships | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | Electron correlation | |||||||||
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| 主題Scheme | Other | |||||||||
| 主題 | Genetics | |||||||||
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| 主題Scheme | Other | |||||||||
| 主題 | Partition coefficient | |||||||||
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| 主題Scheme | Other | |||||||||
| 主題 | Receptors | |||||||||
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| 主題Scheme | Other | |||||||||
| 主題 | Stability | |||||||||
| 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 |
Shirasawa, Raku
× Shirasawa, Raku
× Takaki, Katsushi
× 宮尾, 知幸 |
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| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | In the pursuit of optimal quantitative structure$2013activity relationship (QSAR) models, two key factors are paramount: the robustness of predictive ability and the interpretability of the model. Symbolic regression (SR) searches for the mathematical expressions that explain a training data set. Thus, the models provided by SR are globally interpretable. We previously proposed an SR method that can generate interpretable expressions by humans. This study introduces an enhanced symbolic regression method, termed filter-induced genetic programming 2 (FIGP2), as an extension of our previously proposed SR method. FIGP2 is designed to improve the generalizability of SR models and to be applicable to data sets in which cost-intensive descriptors are employed. The FIGP2 method incorporates two major improvements: a modified domain filter to eradicate diverging expressions based on optimal calculation and the introduction of a stability metric to penalize expressions that would lead to overfitting. Our retrospective comparative analysis using 12 structure$2013activity relationship data sets revealed that FIGP2 surpassed the previously proposed SR method and conventional modeling methods, such as support vector regression and multivariate linear regression in terms of predictive performance. Generated mathematical expressions by FIGP2 were relatively simple and not divergent in the domain of function. Taken together, FIGP2 can be used for making interpretable regression models with predictive ability. | |||||||||
| 書誌情報 |
en : ACS Omega 巻 9, 号 8, p. 9463-9474, 発行日 2024-02-16 |
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| 出版者 | ||||||||||
| 出版者 | American Chemical Society | |||||||||
| ISSN | ||||||||||
| 収録物識別子タイプ | EISSN | |||||||||
| 収録物識別子 | 2470-1343 | |||||||||
| 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/10.1021/acsomega.3c09047 | |||||||||
| 出版者版URI | ||||||||||
| 関連タイプ | isReplacedBy | |||||||||
| 識別子タイプ | URI | |||||||||
| 関連識別子 | https://pubs.acs.org/doi/full/10.1021/acsomega.3c09047 | |||||||||
| 権利 | ||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/ | |||||||||
| 権利情報 | Copyright $00A9 2024 The Authors. Published by American Chemical Society. This publication is licensed under CC-BY 4.0 . | |||||||||
| 著者版フラグ | ||||||||||
| 出版タイプ | NA | |||||||||
