ArticleACS central science2023
Unbiasing Retrosynthesis Language Models with Disconnection Prompts.
Article in ACS central science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A survey on large language models in biology and chemistry.Experimental & molecular medicine · 2026Review
- Data augmentation in a triple transformer loop retrosynthesis model.Digital discovery · 2026Article
- Predicting reaction conditions: a data-driven perspective.Chemical science · 2025Review
- From Spectra to Structure: AI-PoweredAnalytical chemistry · 2025Article
- Negative chemical data boosts language models in reaction outcome prediction.Science advances · 2025Article
- Language models can identify enzymatic binding sites in protein sequences.Computational and structural biotechnology journal · 2024Article
- Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review.ACS synthetic biology · 2024Article
- MetaPredictor: in silico prediction of drug metabolites based on deep language models with prompt engineering.Briefings in bioinformatics · 2024Article
- Leveraging Language Model Multitasking To Predict C-H Borylation Selectivity.Journal of chemical information and modeling · 2024Article
- Multistep retrosynthesis combining a disconnection aware triple transformer loop with a route penalty score guided tree search.Chemical science · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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 human-in-the-loop strategy, we propose a two-stage schema consisting of automatic identification of disconnection sites, followed by prediction of reactant sets, thereby achieving a considerable improvement in class diversity compared with the baseline. The approach is effective in mitigating prediction biases derived from training data. This provides a wider variety of usable building blocks and improves the end user's digital experience. We demonstrate its application to different chemistry domains, from traditional to enzymatic reactions, in which substrate specificity is critical.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.