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 this paper, we design a new method for inferring a polymer based on the framework. For this, we introduce a new way of representing a polymer as a form of monomer and define new descriptors that feature the structure of polymers. We also use linear regression as a building block of constructing a prediction function in the framework. The results of our computational experiments reveal a set of chemical properties on polymers to which a prediction function constructed with linear regression performs well. We also observe that the proposed method can infer polymers with up to 50 non-hydrogen atoms in a monomer form.
@article{arxiv.2109.02628,
title = {A Method for Inferring Polymers Based on Linear Regression and Integer Programming},
author = {Ryota Ido and Shengjuan Cao and Jianshen Zhu and Naveed Ahmed Azam and Kazuya Haraguchi and Liang Zhao and Hiroshi Nagamochi and Tatsuya Akutsu},
journal= {arXiv preprint arXiv:2109.02628},
year = {2021}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2107.02381; text overlap with arXiv:2108.10266