ArticleNature communications2024
Retrosynthesis prediction with an iterative string editing model.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Enhancing biocatalytic retrosynthesis with a graph-to-graph model.Chemical science · 2026Article
- Hyp-retro: hypernetwork-guided preference-conditioned retrosynthetic planner.Molecular diversity · 2026Article
- Transforming Molecular Synthesis With Large Language Models.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- Generation of antifungals to combat drug resistance using language models and diffusion models.Journal of advanced research · 2026Article
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- EnzRetro: Enzymatic Retrosynthetic Planning With Site-Specific Reaction Edits Based on Sequence Generative Architecture.Exploration (Beijing, China) · 2026Article
- From machine learning to multimodal models: The AI revolution in enzyme engineering.Biodesign research · 2026Review
- An artificial intelligence-driven synthesis planning platform (PhotoCat) for photocatalysis.Communications chemistry · 2026Article
- Enhancing deep chemical reaction prediction with advanced chirality and fragment representation.Chemical communications (Cambridge, England) · 2025Article
- Considerations in Pursuing Reaction Scope Generality.Angewandte Chemie (International ed. in English) · 2025Review
- RSGPT: a generative transformer model for retrosynthesis planning pre-trained on ten billion datapoints.Nature communications · 2025Article
- RPSubAlign: a novel sequence-based molecular representation method for retrosynthesis prediction with improved validity and robustness.Briefings in bioinformatics · 2025Article
- A Perspective on Foundation Models in Chemistry.JACS Au · 2025Review
- Review
- Application of Transformers to Chemical Synthesis.Molecules (Basel, Switzerland) · 2025Review
- Retrosynthesis prediction with an iterative string editing model.Nature communications · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Retrosynthesis is a crucial task in drug discovery and organic synthesis, where artificial intelligence (AI) is increasingly employed to expedite the process. However, existing approaches employ token-by-token decoding methods to translate target molecule strings into corresponding precursors, exhibiting unsatisfactory performance and limited diversity. As chemical reactions typically induce local molecular changes, reactants and products often overlap significantly. Inspired by this fact, we propose reframing single-step retrosynthesis prediction as a molecular string editing task, iteratively refining target molecule strings to generate precursor compounds. Our proposed approach involves a fragment-based generative editing model that uses explicit sequence editing operations. Additionally, we design an inference module with reposition sampling and sequence augmentation to enhance both prediction accuracy and diversity. Extensive experiments demonstrate that our model generates high-quality and diverse results, achieving superior performance with a promising top-1 accuracy of 60.8% on the standard benchmark dataset USPTO-50 K.
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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.