Using Machine Learning To Predict Suitable Conditions for Organic Reactions.

ABSTRACT: Reaction condition recommendation is an essential element for the realization of computer-assisted synthetic planning. Accurate suggestions of reaction conditions are required for experimental validation and can have a significant effect on the success or failure of an attempted transformation. However, de novo condition recommendation remains a challenging and under-explored problem and relies heavily on chemists' knowledge and experience. In this work, we develop a neural-network model to predict the chemical context (catalyst(s), solvent(s), reagent(s)), as well as the temperature most suitable for any particular organic reaction. Trained on ∼10 million examples from Reaxys, the model is able to propose conditions where a close match to the recorded catalyst, solvent, and reagent is fou

SUBMITTER: Gao H 

PROVIDER: S-EPMC6276053 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature