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arXiv · 2108.10266

Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Search

Naveed Ahmed Azam·Jianshen Zhu·Kazuya Haraguchi·Liang Zhao·Hiroshi Nagamochi·Tatsuya Akutsu
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Abstract

A novel framework has recently been proposed for designing the molecular structure of chemical compounds with a desired chemical property using both artificial neural networks and mixed integer linear programming. In the framework, a chemical graph with a target chemical value is inferred as a feasible solution of a mixed integer linear program that represents a prediction function and other requirements on the structure of graphs. In this paper, we propose a procedure for generating other feasible solutions of the mixed integer linear program by searching the neighbor of output chemical graph in a search space. The procedure is combined in the framework as a new building block. The results of our computational experiments suggest that the proposed method can generate an additional number of new chemical graphs with up to 50 non-hydrogen atoms.

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Naveed Ahmed Azam, Jianshen Zhu, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu. 2021-08-23. Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Search. https://arxiv.org/abs/2108.10266

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