Conditional Variational AutoEncoder to Predict Suitable Conditions for Hydrogenation Reactions.

ABSTRACT: Reaction conditions (RCs) are a crucial part of reaction definition, and their accurate prediction is an important component of chemical synthesis planning. The existence of multiple combinations of RCs capable of achieving the desired result complicates the task of condition recommendation. Herein, we propose a conditional variational autoencoder (CVAE) generative model to predict suitable RCs. The CVAE model has been customized to generate diverse sets of valid conditions, ensuring high flexibility and accuracy, while circumventing the necessity for enumeration or combinatorial search of potential RCs. The efficacy of the CVAE approaches was evaluated using hydrogenation reactions and other H₂-mediated reactions, predicting the set of catalysts, additives (acid, base, and cata

SUBMITTER: Mazitov D 

PROVIDER: S-EPMC12786955 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature