Unbiasing Retrosynthesis Language Models with Disconnection Prompts.

ABSTRACT: Data-driven approaches to retrosynthesis are limited in user interaction, diversity of their predictions, and recommendation of unintuitive disconnection strategies. Herein, we extend the notions of prompt-based inference in natural language processing to the task of chemical language modeling. We show that by using a prompt describing the disconnection site in a molecule we can steer the model to propose a broader set of precursors, thereby overcoming training data biases in retrosynthetic recommendations and achieving a 39% performance improvement over the baseline. For the first time, the use of a disconnection prompt empowers chemists by giving them greater control over the disconnection predictions, which results in more diverse and creative recommendations. In addition, in place of a

SUBMITTER: Thakkar A 

PROVIDER: S-EPMC10390024 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature