Initial Sample Selection in Bayesian Optimization for Combinatorial Optimization of Chemical Compounds.

ABSTRACT: An efficient search for optimal solutions in Bayesian optimization (BO) entails providing appropriate initial samples when building a Gaussian process regression model. For general experimental designs without compounds or molecular descriptors in explanatory variable x, selecting initial samples with a larger D-optimality allows little correlation between x in the selected samples, which leads to effective regression model building. However, in the case of experimental designs with compounds, a high correlation always exists between molecular descriptors calculated from chemical structures, and compounds with similar structures form clusters in the chemical space. Therefore, selecting the initial samples uniformly from each cluster is desirable for obtaining initial samples

SUBMITTER: Morishita T 

PROVIDER: S-EPMC9850731 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature