Development of Nonlinear Regression Method for Extrapolation and Application to Efficient Experimental Parameter Search

Abstract

In the field of material development and drug development, it is necessary to search for a compound that satisfies the desired properties and activity from a large number of candidates. There is a need to reduce the number of experiments in order to improve financial and time costs. Sequential Model-Based Optimization (SMBO) is one of the methods to reduce the number of experiments by using machine learning based prediction models. Conventional methods used as models are not suitable for extrapolation. On the other hand, extrapolation is required in compound discovery to achieve properties and performance independent of existing data. In this study, we propose a new nonlinear regression method, Stochastic Threshold Model Trees (STMT), which is applicable to extrapolation, and apply it to SMBO to achieve efficient compound search. By applying a new acquisition function to STMT, we have shown that the search performance of the proposed method was better than that of the conventional methods for the dataset used for verification. We also visualized the search process of each method and confirmed that the proposed method is efficient.