A Method for Inferring Polymers Based on Linear Regression and Integer Programming
| Autor: | Ido, Ryota, Cao, Shengjuan, Zhu, Jianshen, Azam, Naveed Ahmed, Haraguchi, Kazuya, Zhao, Liang, Nagamochi, Hiroshi, Akutsu, Tatsuya |
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| Rok vydání: | 2021 |
| Předmět: | |
| 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 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. Comment: arXiv admin note: substantial text overlap with arXiv:2107.02381; text overlap with arXiv:2108.10266 |
| Databáze: | arXiv |
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