Abstract
A novel framework for inverse quantitative structure–activity relationships (inverse QSAR) has recently been proposed and developed using both artificial neural networks and mixed integer linear programming. However, classes of chemical graphs treated by the framework are limited. In order to deal with an arbitrary graph in the framework, we introduce a new model, called a two-layered model, and develop a corresponding method. In this model, each chemical graph is regarded as two parts: the exterior and the interior. The exterior consists of maximal acyclic induced subgraphs with bounded height, the interior is the connected subgraph obtained by ignoring the exterior, and the feature vector consists of the frequency of adjacent atom pairs in the interior and the frequency of chemical acyclic graphs in the exterior. Our method is more flexible than the existing method in the sense that any type of graphs can be inferred. We compared the proposed method with an existing method using several data sets obtained from PubChem database. The new method could infer more general chemical graphs with up to 50 non-hydrogen atoms. The proposed inverse QSAR method can be applied to the inference of more general chemical graphs than before.
Funder
Japan Society for the Promotion of Science
Subject
Inorganic Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Computer Science Applications,Spectroscopy,Molecular Biology,General Medicine,Catalysis
Cited by
6 articles.
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1. An Inverse QSAR Method Based on Linear Regression and Integer Programming;Frontiers in Bioscience-Landmark;2022-06-10
2. A Method for Molecular Design Based on Linear Regression and Integer Programming;2022 12th International Conference on Bioscience, Biochemistry and Bioinformatics;2022-01-07
3. Adjustive Linear Regression and Its Application to the Inverse QSAR;Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies;2022
4. Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Search;2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2021-12-09
5. 機械学習QSARの整数計画法に基づく逆解析法;Journal of Computer Chemistry, Japan;2021