Single-step retrosynthesis prediction by leveraging commonly preserved substructures.

ABSTRACT: Retrosynthesis analysis is an important task in organic chemistry with numerous industrial applications. Previously, machine learning approaches employing natural language processing techniques achieved promising results in this task by first representing reactant molecules as strings and subsequently predicting reactant molecules using text generation or machine translation models. Chemists cannot readily derive useful insights from traditional approaches that rely largely on atom-level decoding in the string representations, because human experts tend to interpret reactions by analyzing substructures that comprise a molecule. It is well-established that some substructures are stable and remain unchanged in reactions. In this paper, we developed a substructure-level decoding model, where

SUBMITTER: Fang L 

PROVIDER: S-EPMC10147675 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature