Molecular Design Based on Integer Programming and Splitting Data Sets by Hyperplanes
This work addresses the challenge of constructing accurate prediction functions for molecular design, which is incremental as it builds on an existing framework by introducing a data-splitting technique.
The authors tackled the problem of designing molecular structures with desired chemical properties by proposing a method that splits a dataset using hyperplanes to improve prediction functions, resulting in enhanced learning performance for several challenging chemical properties.
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 $θ$. We construct a prediction function $ψ$ to the data set $\mathcal{C}$ by combining prediction functions $ψ_i,i=1,2$ 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.