Developing a Design Guideline of Boronic Acid Derivatives to Scavenge Targeted Sugars in the Formose Reaction Products using DFT-based Machine Learning

Abstract

Formose reaction facilitates the synthesis of sugars from HCHO, yet the valuable sugars constitute only a small portion of the total products. This necessitates the need for a chemical scavenger capable of selectively capturing only valuable sugars. With over 600,000 potential combinations of boronic acid-based scavengers available, pursuing a deductive search approach is unfeasible. This study aims to derive guidelines for designing scavengers that readily bind with target sugars while avoiding non-target ones, via machine learning informed by DFT calculations.