Reagent prediction with a molecular transformer improves reaction data quality.

ABSTRACT: Automated synthesis planning is key for efficient generative chemistry. Since reactions of given reactants may yield different products depending on conditions such as the chemical context imposed by specific reagents, computer-aided synthesis planning should benefit from recommendations of reaction conditions. Traditional synthesis planning software, however, typically proposes reactions without specifying such conditions, relying on human organic chemists who know the conditions to carry out suggested reactions. In particular, reagent prediction for arbitrary reactions, a crucial aspect of condition recommendation, has been largely overlooked in cheminformatics until recently. Here we employ the Molecular Transformer, a state-of-the-art model for reaction prediction and single-step retro

SUBMITTER: Andronov M 

PROVIDER: S-EPMC10034139 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature