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arXiv · 2109.02628

A Method for Inferring Polymers Based on Linear Regression and Integer Programming

Ryota Ido·Shengjuan Cao·Jianshen Zhu·Naveed Ahmed Azam·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 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.

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Ryota Ido, Shengjuan Cao, Jianshen Zhu, Naveed Ahmed Azam, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu. 2021-08-24. A Method for Inferring Polymers Based on Linear Regression and Integer Programming. https://arxiv.org/abs/2109.02628

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