ArticleNature communications2022
Retrosynthetic reaction pathway prediction through neural machine translation of atomic environments.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- A survey on large language models in biology and chemistry.Experimental & molecular medicine · 2026Review
- Collision-free morgan fingerprints: a principled approach to enhance machine learning performance and interpretability in chemistry.Journal of cheminformatics · 2026Article
- Antiviral drug discovery and development: challenges and future directions.Signal transduction and targeted therapy · 2026Review
- DeepRetro discovers retrosynthetic pathways through iterative large language model reasoning.Scientific reports · 2026Article
- Optimization of potential targets for antidepressant Chinese medicines: AI and multi-omics methods.Chinese medicine · 2026Review
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- A generative artificial intelligence approach for the discovery of antimicrobial peptides against multidrug-resistant bacteria.Nature microbiology · 2025Article
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
- Evaluation of LLM-generated peptide as foundation template for discovery of effective encrypted AMPs against clinical superbugs.Microbiology spectrum · 2025Article
- Graph-Theory Algorithm for Prediction of Electrolyte Degradation Reactions in Lithium- and Sodium-Ion Batteries.Materials (Basel, Switzerland) · 2025Article
- Single-step retrosynthesis prediction via multitask graph representation learning.Nature communications · 2025Article
- Sort & Slice: a simple and superior alternative to hash-based folding for extended-connectivity fingerprints.Journal of cheminformatics · 2024Article
- A systematic review of deep learning chemical language models in recent era.Journal of cheminformatics · 2024Review
- Site-specific template generative approach for retrosynthetic planning.Nature communications · 2024Article
- RetroCaptioner: beyond attention in end-to-end retrosynthesis transformer via contrastively captioned learnable graph representation.Bioinformatics (Oxford, England) · 2024Article
- Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review.ACS synthetic biology · 2024Article
- Developing BioNavi for Hybrid Retrosynthesis Planning.JACS Au · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Designing efficient synthetic routes for a target molecule remains a major challenge in organic synthesis. Atom environments are ideal, stand-alone, chemically meaningful building blocks providing a high-resolution molecular representation. Our approach mimics chemical reasoning, and predicts reactant candidates by learning the changes of atom environments associated with the chemical reaction. Through careful inspection of reactant candidates, we demonstrate atom environments as promising descriptors for studying reaction route prediction and discovery. Here, we present a new single-step retrosynthesis prediction method, viz. RetroTRAE, being free from all SMILES-based translation issues, yields a top-1 accuracy of 58.3% on the USPTO test dataset, and top-1 accuracy reaches to 61.6% with the inclusion of highly similar analogs, outperforming other state-of-the-art neural machine translation-based methods. Our methodology introduces a novel scheme for fragmental and topological descriptors to be used as natural inputs for retrosynthetic prediction tasks.
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