Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Search
| Autor: | Azam, Naveed Ahmed, Zhu, Jianshen, Haraguchi, Kazuya, Zhao, Liang, Nagamochi, Hiroshi, Akutsu, Tatsuya |
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| Rok vydání: | 2021 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: arXiv admin note: substantial text overlap with arXiv:2107.02381 |
| Databáze: | arXiv |
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