ArticleChemical science2020
Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy.
Article in Chemical science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 123 papers.
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Who cites it
123 citing papers in PubMed.
- AI-Driven Synthesis in Medicinal Chemistry: Integrating Large Language Models, Robotic Automation, and Sustainability Metrics to Accelerate Drug Discovery.Medicinal research reviews · 2026Review
- Enhancing biocatalytic retrosynthesis with a graph-to-graph model.Chemical science · 2026Article
- Quantifying the failure modes of current one-step retrosynthesis models.Chemical science · 2026Article
- A Return to the Logic-First Spirit of Corey's Retrosynthetic Analysis, Now Implemented in Modern Data-Driven CASP.ACS central science · 2026Article
- A Multimodal Representation Learning Framework for Molecular Graph and NMR Spectrum Alignment.Entropy (Basel, Switzerland) · 2026Article
- SynRXN: An Open Benchmark and Curated Dataset for Computational Reaction Modeling.Scientific data · 2026Article
- Yield Prediction of Organic Reactions in Biased Data Sets via Positive-Unlabeled Learning.Journal of the American Chemical Society · 2026Article
- A survey on large language models in biology and chemistry.Experimental & molecular medicine · 2026Review
- Higher-Level Strategies for Computer-Aided Retrosynthesis.ACS central science · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- TANGO: direct optimization of constrained synthesizability for generative molecular design.Nature computational science · 2026Article
- Data augmentation in a triple transformer loop retrosynthesis model.Digital discovery · 2026Article
- An artificial intelligence-driven synthesis planning platform (PhotoCat) for photocatalysis.Communications chemistry · 2026Article
- Conditional Variational AutoEncoder to Predict Suitable Conditions for Hydrogenation Reactions.Molecules (Basel, Switzerland) · 2025Article
- A virtual platform for automated hybrid organic-enzymatic synthesis planning.Nature communications · 2025Article
- Data-driven recommendation of agents, temperature, and equivalence ratios for organic synthesis.Chemical science · 2025Article
- Predicting reaction conditions: a data-driven perspective.Chemical science · 2025Review
- Computational tools for nonnatural pathway design: Algorithms, applications, and challenges.Biodesign research · 2025Review
- Transfer Learning for Heterocycle Retrosynthesis.Journal of chemical information and modeling · 2025Article
- novoStoic2.0: An integrated framework for pathway synthesis, thermodynamic evaluation, and enzyme selection.PLoS computational biology · 2025Article
63 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present an extension of our Molecular Transformer model combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention. The single-step retrosynthetic model sets a new state of the art for predicting reactants as well as reagents, solvents and catalysts for each retrosynthetic step. We introduce four metrics (coverage, class diversity, round-trip accuracy and Jensen-Shannon divergence) to evaluate the single-step retrosynthetic models, using the forward prediction and a reaction classification model always based on the transformer architecture. The hypergraph is constructed on the fly, and the nodes are filtered and further expanded based on a Bayesian-like probability. We critically assessed the end-to-end framework with several retrosynthesis examples from literature and academic exams. Overall, the frameworks have an excellent performance with few weaknesses related to the training data. The use of the introduced metrics opens up the possibility to optimize entire retrosynthetic frameworks by focusing on the performance of the single-step model only.
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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.