| Autor: |
Ido R, Cao S, Zhu J, Azam NA, Haraguchi K, Zhao L, Nagamochi H, Akutsu T |
| Jazyk: |
angličtina |
| Zdroj: |
IEEE/ACM transactions on computational biology and bioinformatics [IEEE/ACM Trans Comput Biol Bioinform] 2024 Aug 22; Vol. PP. Date of Electronic Publication: 2024 Aug 22. |
| DOI: |
10.1109/TCBB.2024.3447780 |
| Abstrakt: |
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 nonhydrogen atoms in a monomer form. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
|
