BiDir-GRCO: A Bidirectional General Reaction Conditions Optimization Framework Integrating Multi-Armed Bandit and Regression Model.

ABSTRACT: In the research and industrial production of chemical synthesis, identifying suitable general reaction conditions is critical. However, chemical reactions typically involve multiple factors, including catalysts, solvents, temperature, and reaction time, and the optimal conditions for a single substrate are often not applicable to others. To address this limitation, this study proposes a bidirectional general reaction condition optimization framework that integrates the multiarmed bandit algorithm and regression model. The framework first utilizes the multiarmed bandit algorithm to dynamically balance exploration and exploitation in reaction condition selection. Then, a regression model, combined with molecular representation and a per-substrate selective model training strategy, is used to

SUBMITTER: Jiang Q 

PROVIDER: S-EPMC12368621 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature