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.
@article{arxiv.2108.10266,
title = {Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Search},
author = {Naveed Ahmed Azam and Jianshen Zhu and Kazuya Haraguchi and Liang Zhao and Hiroshi Nagamochi and Tatsuya Akutsu},
journal= {arXiv preprint arXiv:2108.10266},
year = {2021}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2107.02381