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.
Categories: cs.CE cs.AI cs.LG