ArticleNature communications2022
Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP.
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 59 papers.
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Who cites it
59 citing papers in PubMed.
- Enhancing biocatalytic retrosynthesis with a graph-to-graph model.Chemical science · 2026Article
- Systematic prioritization of candidate genes in camptothecin biosynthesis using multi-omics and deep learning.The plant genome · 2026Article
- Integrating Biosynthetic, Genomic and Ecological Open Data for Medicinal Plant Research: A Leakage-Aware Evidence-Prioritization Framework.International journal of molecular sciences · 2026Article
- TridentSynth: a webtool for the retrosynthesis of molecules using chimeric type I polyketide synthases and chemoenzymatic pathways.Nucleic acids research · 2026Article
- Probiotics and Postbiotics in Life-Style Disease Management: A Comprehensive Review on the Technologies in the Era of Omics and Artificial Intelligence.Probiotics and antimicrobial proteins · 2026Review
- Mechanism-informed rules tunably balance novelty and feasibility of predicted enzymatic reactions.bioRxiv : the preprint server for biology · 2026Article
- Block-building with yeast to elucidate an artificial pathway for de novo biosynthesis of glabridin.Nature communications · 2026Article
- EnzRetro: Enzymatic Retrosynthetic Planning With Site-Specific Reaction Edits Based on Sequence Generative Architecture.Exploration (Beijing, China) · 2026Article
- Review
- Enzyformer: a two-stage pretrained model for enzymatic retrosynthesis.Journal of cheminformatics · 2026Article
- RetroRules 2026: an expanded database combining biochemical and organic reaction templates for pathway discovery.Nucleic acids research · 2026Article
- Natural novel vitamin C derivative, 2-Chinese herbal medicines · 2026Review
- AI-enabled fungal biomanufacturing: from genome mining to the artificial intelligence virtual cell.Mycology · 2026Review
- Productive chaos and precision engineering: decoupling discovery from manufacturing to revolutionize plant-inspired therapeutics.Frontiers in plant science · 2026Article
- Emerging strategies to enhance microbial natural product-based drug discovery.Current opinion in biotechnology · 2025Review
- Artificial intelligence-curated repository of gene-encoded natural diverse components from herbal medicines.Innovation (Cambridge (Mass.)) · 2025Article
- A virtual platform for automated hybrid organic-enzymatic synthesis planning.Nature communications · 2025Article
- Computational approaches to enzymatic reaction assignment: a review of methods, validations, and future directions.Briefings in bioinformatics · 2025Review
- The biosynthesis and diversity of taxanes: From pathway elucidation to engineering and synthetic biology.Plant communications · 2025Review
- Metabolic Engineering of Terpenoid Biosynthesis in Medicinal Plants: From Genomic Insights to Biotechnological Applications.Current issues in molecular biology · 2025Review
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The complete biosynthetic pathways are unknown for most natural products (NPs), it is thus valuable to make computer-aided bio-retrosynthesis predictions. Here, a navigable and user-friendly toolkit, BioNavi-NP, is developed to predict the biosynthetic pathways for both NPs and NP-like compounds. First, a single-step bio-retrosynthesis prediction model is trained using both general organic and biosynthetic reactions through end-to-end transformer neural networks. Based on this model, plausible biosynthetic pathways can be efficiently sampled through an AND-OR tree-based planning algorithm from iterative multi-step bio-retrosynthetic routes. Extensive evaluations reveal that BioNavi-NP can identify biosynthetic pathways for 90.2% of 368 test compounds and recover the reported building blocks as in the test set for 72.8%, 1.7 times more accurate than existing conventional rule-based approaches. The model is further shown to identify biologically plausible pathways for complex NPs collected from the recent literature. The toolkit as well as the curated datasets and learned models are freely available to facilitate the elucidation and reconstruction of the biosynthetic pathways for NPs.
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