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Symbolic regression for the interpretation of quantitative structure-property relationships
http://hdl.handle.net/10061/0002000433
http://hdl.handle.net/10061/0002000433e8bb37b3-ac98-4cd6-9a9b-73e4f5bfa19f
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||
|---|---|---|---|---|---|---|---|---|
| 公開日 | 2024-05-28 | |||||||
| タイトル | ||||||||
| タイトル | Symbolic regression for the interpretation of quantitative structure-property relationships | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Model interpretability | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Quantitative structure-activity relationships | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Quantitative structure-property relationships | |||||||
| キーワード | ||||||||
| 主題Scheme | UDC | |||||||
| 主題 | Symbolic regression | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Genetic programming | |||||||
| 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 |
Takaki, Katsushi
× Takaki, Katsushi
× 宮尾, 知幸 |
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| 抄録 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | The interpretation of quantitative structure$2013activity or structure$2013property relationships is important in the field of chemoinformatics. Although multivariate linear regression models are typically interpretable, they do not generally have high predictive abilities. Symbolic regression (SR) combined with genetic programming (GP) is a well-established technique for generating the mathematical expressions that describe the relationships within a dataset. However, SR sometimes produces complicated expressions that are hard for humans to interpret. This paper proposes a method for generating simpler expressions by incorporating three filters into GP-based SR. The filters are further combined with nonlinear least-squares optimization to give filter-introduced GP (FIGP), which improves the predictive ability of SR models while retaining simple expressions. As a proof-of-concept, the quantitative estimate of drug-likeness and the synthetic accessibility score are predicted based on the chemical structures of compounds. Overall, FIGP generates less-complicated expressions than previous SR methods. In terms of predictive ability, FIGP is better than GP, but is outperformed by a support vector machine with a radial basis function kernel. Furthermore, quantitative structure$2013activity relationship models are constructed for three matching molecular series with biological targets. In the case of one target, the activity prediction models given by FIGP exhibit better predictive ability than multivariate linear regression and support vector regression with the radial basis function kernel, whereas for the remaining cases, FIGP is slightly less accurate than multivariate linear regression. | |||||||
| 書誌情報 |
en : Artificial Intelligence in the Life Sciences 巻 2, 発行日 2022-11-05 |
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| 出版者 | ||||||||
| 出版者 | Elsevier | |||||||
| ISSN | ||||||||
| 収録物識別子タイプ | EISSN | |||||||
| 収録物識別子 | 2667-3185 | |||||||
| 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/10.1016/j.ailsci.2022.100046 | |||||||
| 出版者版URI | ||||||||
| 関連タイプ | isReplacedBy | |||||||
| 識別子タイプ | URI | |||||||
| 関連識別子 | https://www.sciencedirect.com/science/article/pii/S2667318522000162?via%3Dihub | |||||||
| 権利 | ||||||||
| 権利情報Resource | http://creativecommons.org/licenses/by/4.0/ | |||||||
| 権利情報 | $00A9 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) | |||||||
| 著者版フラグ | ||||||||
| 出版タイプ | NA | |||||||
