Transformer Performance for Chemical Reactions: Analysis of Different Predictive and Evaluation Scenarios.

ABSTRACT: The prediction of chemical reaction pathways has been accelerated by the development of novel machine learning architectures based on the deep learning paradigm. In this context, deep neural networks initially designed for language translation have been used to accurately predict a wide range of chemical reactions. Among models suited for the task of language translation, the recently introduced molecular transformer reached impressive performance in terms of forward-synthesis and retrosynthesis predictions. In this study, we first present an analysis of the performance of transformer models for product, reactant, and reagent prediction tasks under different scenarios of data availability and data augmentation. We find that the impact of data augmentation depends on the prediction task and

SUBMITTER: Jaume-Santero F 

PROVIDER: S-EPMC10091402 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature