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Molecular Design Based on Integer Programming and Splitting Data Sets by Hyperplanes
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DOI：10.1109/TCBB.2024.3402675.png)
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A novel framework for designing the molecular structure of chemical compounds with a desired chemical property has recently been proposed. The framework infers a desired chemical graph by solving a mixed integer linear program (MILP) that simulates the computation process of two functions: a feature function defined by a two-layered model on chemical graphs and a prediction function constructed by a machine learning method. To improve the learning performance of prediction functions in the framework, we design a method that splits a given data set C into two subsets C-(i), i = 1, 2 by a hyperplane in a chemical space so that most compounds in the first (resp., second) subset have observed values lower (resp., higher) than a threshold theta. We construct a prediction function psi to the data set C by combining prediction functions psi(i), i = 1, 2 each of which is constructed on C-(i) independently. The results of our computational experiments suggest that the proposed method improved the learning performance for several chemical properties to which a good prediction function has been difficult to construct.
Keyword:
Machine learning
integer programming
chemo-informatics
materials informatics
QSAR/QSPR
molecular design
期刊
I
IF:
3.4
论文数:
3.3K
被引数:
6.4K
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