ArticleJACS Au2024
Node-Aligned Graph-to-Graph: Elevating Template-free Deep Learning Approaches in Single-Step Retrosynthesis.
Article in JACS Au, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Enhancing biocatalytic retrosynthesis with a graph-to-graph model.Chemical science · 2026Article
- ConfRetro: a 3D-aware template-free method for enhancing retrosynthesis via molecular conformer information.Bioinformatics (Oxford, England) · 2026Article
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- An artificial intelligence-driven synthesis planning platform (PhotoCat) for photocatalysis.Communications chemistry · 2026Article
- Graph-sequence enhanced transformer for template-free prediction of natural product biosynthesis.Patterns (New York, N.Y.) · 2025Article
- RSGPT: a generative transformer model for retrosynthesis planning pre-trained on ten billion datapoints.Nature communications · 2025Article
- Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 2025Review
- Generating diversity and securing completeness in algorithmic retrosynthesis.Journal of cheminformatics · 2025Article
- Article
- Deep learning in template-free de novo biosynthetic pathway design of natural products.Briefings in bioinformatics · 2024Review
- Ualign: pushing the limit of template-free retrosynthesis prediction with unsupervised SMILES alignment.Journal of cheminformatics · 2024Article
- AutoTemplate: enhancing chemical reaction datasets for machine learning applications in organic chemistry.Journal of cheminformatics · 2024Article
- The recent advances in the approach of artificial intelligence (AI) towards drug discovery.Frontiers in chemistry · 2024Review
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Single-step retrosynthesis in organic chemistry increasingly benefits from deep learning (DL) techniques in computer-aided synthesis design. While template-free DL models are flexible and promising for retrosynthesis prediction, they often ignore vital 2D molecular information and struggle with atom alignment for node generation, resulting in lower performance compared to the template-based and semi-template-based methods. To address these issues, we introduce node-aligned graph-to-graph (NAG2G), a transformer-based template-free DL model. NAG2G combines 2D molecular graphs and 3D conformations to retain comprehensive molecular details and incorporates product-reactant atom mapping through node alignment, which determines the order of the node-by-node graph outputs process in an autoregressive manner. Through rigorous benchmarking and detailed case studies, we have demonstrated that NAG2G stands out with its remarkable predictive accuracy on the expansive data sets of USPTO-50k and USPTO-FULL. Moreover, the model's practical utility is underscored by its successful prediction of synthesis pathways for multiple drug candidate molecules. This proves not only NAG2G's robustness but also its potential to revolutionize the prediction of complex chemical synthesis processes for future synthetic route design tasks.
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