A Method for the Inverse QSAR/QSPR Based on Artificial Neural Networks and Mixed Integer Linear Programming

• Item type: SIG Technical Reports(1)
• 公開日: 2019-06-10
• タイトル: A Method for the Inverse QSAR/QSPR Based on Artificial Neural Networks and Mixed Integer Linear Programming
• タイトル / 言語: en
• タイトル: A Method for the Inverse QSAR/QSPR Based on Artificial Neural Networks and Mixed Integer Linear Programming
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Graduate School of Informatics, Kyoto University
• 著者所属: Graduate School of Informatics, Kyoto University
• 著者所属: Graduate School of Informatics, Kyoto University
• 著者所属: Graduate School of Informatics, Kyoto University
• 著者所属: Graduate School of Informatics, Kyoto University
• 著者所属: Bioinformatics Center, Institute for Chemical Research, Kyoto University
• 著者所属(英): en
• 著者所属(英): Graduate School of Informatics, Kyoto University
• 著者所属(英): en
• 著者所属(英): Graduate School of Informatics, Kyoto University
• 著者所属(英): en
• 著者所属(英): Graduate School of Informatics, Kyoto University
• 著者所属(英): en
• 著者所属(英): Graduate School of Informatics, Kyoto University
• 著者所属(英): en
• 著者所属(英): Graduate School of Informatics, Kyoto University
• 著者所属(英): en
• 著者所属(英): Bioinformatics Center, Institute for Chemical Research, Kyoto University
• 著者名: Rachaya, Chiewvanichakorn; Chenxi, Wang; Zhe, Zhang; Aleksandar, Shurbevski; Hiroshi, Nagamochi; Tatsuya, Akutsu
• 著者名(英): Rachaya, Chiewvanichakorn; Chenxi, Wang; Zhe, Zhang; Aleksandar, Shurbevski; Hiroshi, Nagamochi; Tatsuya, Akutsu
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In this study, we propose a novel method for the inverse QSAR/QSPR. Given a set of chemical compounds G and their values a(G) of a chemical property, we define a feature vector f(G) of each chemical compound G. By using a set of feature vectors as training data, the first phase of our method constructs a prediction function ψwith an artificial neural network (ANN) so that ψ(f(G)) takes a value nearly equal to a(G) for many chemical compounds G in the set. Given a target value a* of the chemical property, the second phase infers a chemical structure G* such that a(G*) = a* in the following way. We compute a vector f* such that ψ(f*) = a*, where finding such a vector f* is formulated as a mixed integer linear programming problem (MILP). Finally we generate a chemical structure G* such that f(G*) = f*. For acyclic chemical compounds and some chemical properties such as heat of formation, boiling point, and retention time, we conducted some computational experiments with our method.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In this study, we propose a novel method for the inverse QSAR/QSPR. Given a set of chemical compounds G and their values a(G) of a chemical property, we define a feature vector f(G) of each chemical compound G. By using a set of feature vectors as training data, the first phase of our method constructs a prediction function ψwith an artificial neural network (ANN) so that ψ(f(G)) takes a value nearly equal to a(G) for many chemical compounds G in the set. Given a target value a* of the chemical property, the second phase infers a chemical structure G* such that a(G*) = a* in the following way. We compute a vector f* such that ψ(f*) = a*, where finding such a vector f* is formulated as a mixed integer linear programming problem (MILP). Finally we generate a chemical structure G* such that f(G*) = f*. For acyclic chemical compounds and some chemical properties such as heat of formation, boiling point, and retention time, we conducted some computational experiments with our method.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN10505667
• 書誌情報: 研究報告数理モデル化と問題解決（MPS）; 巻 2019-MPS-123, 号 51, p. 1-7, 発行日 2019-06-10
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8833
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会