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A Method for Inferring Polymers Based on Linear Regression and Integer Programming
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DOI：10.1109/TCBB.2024.3447780.png)
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摘要
En 中文
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.
Keyword:
Cheminformatics
integer programming
linear regression
machine learning
materials informatics
molecular design
polymers
QSAR/QSPR
integer programming
linear regression
machine learning
materials informatics
molecular design
polymers
QSAR/QSPR
期刊
I
IF:
3.4
论文数:
3.3K
被引数:
6.4K
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