Enhancing chemical synthesis: a two-stage deep neural network for predicting feasible reaction conditions.

ABSTRACT: In the field of chemical synthesis planning, the accurate recommendation of reaction conditions is essential for achieving successful outcomes. This work introduces an innovative deep learning approach designed to address the complex task of predicting appropriate reagents, solvents, and reaction temperatures for chemical reactions. Our proposed methodology combines a multi-label classification model with a ranking model to offer tailored reaction condition recommendations based on relevance scores derived from anticipated product yields. To tackle the challenge of limited data for unfavorable reaction contexts, we employed the technique of hard negative sampling to generate reaction conditions that might be mistakenly classified as suitable, forcing the model to refine its decision bounda

SUBMITTER: Chen LY 

PROVIDER: S-EPMC11301986 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature