Improving the performance of models for one-step retrosynthesis through re-ranking.

ABSTRACT: Retrosynthesis is at the core of organic chemistry. Recently, the rapid growth of artificial intelligence (AI) has spurred a variety of novel machine learning approaches for data-driven synthesis planning. These methods learn complex patterns from reaction databases in order to predict, for a given product, sets of reactants that can be used to synthesise that product. However, their performance as measured by the top-N accuracy in matching published reaction precedents still leaves room for improvement. This work aims to enhance these models by learning to re-rank their reactant predictions. Specifically, we design and train an energy-based model to re-rank, for each product, the published reaction as the top suggestion and the remaining reactant predictions as lower-ranked. We show that

SUBMITTER: Lin MH 

PROVIDER: S-EPMC8922884 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature