Molecular Design Based on Integer Programming and Splitting Data Sets by Hyperplanes

• URL: http://arxiv.org/abs/2305.00801v1
• Date: Thu, 27 Apr 2023 04:18:41 GMT
• Title: Molecular Design Based on Integer Programming and Splitting Data Sets by Hyperplanes
• Authors: Jianshen Zhu, Naveed Ahmed Azam, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi and Tatsuya Akutsu
• Abstract summary: We propose a framework for designing the molecular structure of chemical compounds with a desired chemical property. The proposed framework infers a desired chemical graph by solving a mixed integer linear program (MILP) and a prediction function constructed by a machine learning method. 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.
• Score: 6.504869613326338
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 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 $\mathcal{C}$ into two subsets $\mathcal{C}^{(i)},i=1,2$ 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 $\mathcal{C}$ by combining prediction functions $\psi_i,i=1,2$ each of which is constructed on $\mathcal{C}^{(i)}$ 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.

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