http://swrc.ontoware.org/ontology#TechnicalReport
A Method for the Inverse QSAR/QSPR Based on Artificial Neural Networks and Mixed Integer Linear Programming
en
Graduate School of Informatics, Kyoto University
Graduate School of Informatics, Kyoto University
Graduate School of Informatics, Kyoto University
Graduate School of Informatics, Kyoto University
Graduate School of Informatics, Kyoto University
Bioinformatics Center, Institute for Chemical Research, Kyoto University
Rachaya Chiewvanichakorn
Chenxi Wang
Zhe Zhang
Aleksandar Shurbevski
Hiroshi Nagamochi
Tatsuya Akutsu
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.
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.
AN10505667
研究報告数理モデル化と問題解決（MPS）
2019-MPS-123
51
1-7
2019-06-10
2188-8833