ArticlePatterns (New York, N.Y.)2025
Graph-sequence enhanced transformer for template-free prediction of natural product biosynthesis.
Article in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Enhancing biocatalytic retrosynthesis with a graph-to-graph model.Chemical science · 2026Article
- MetaNetX: a bridge between metabolic resources for enhanced curation and multi-omics data harmonization.Nucleic acids research · 2026Article
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
- Computational tools for nonnatural pathway design: Algorithms, applications, and challenges.Biodesign research · 2025Review
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Authors and funding
7 authors.
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Abstract
Natural products (NPs) play a vital role in drug discovery, with many FDA-approved drugs derived from these compounds. Despite their significance, the biosynthetic pathways of NPs remain poorly characterized due to their inherent complexity and the limitations of traditional retrosynthesis methods in predicting such intricate reactions. While template-free machine learning models have demonstrated promise in organic synthesis, their application to biosynthetic pathways is still in its infancy. Addressing this gap, we propose the graph-sequence enhanced transformer (GSETransformer), which leverages both graph structural information and sequential dependencies to achieve superior performance in addressing the complexity of biosynthetic data. When evaluated on benchmark datasets, GSETransformer achieves state-of-the-art performance in single- and multi-step retrosynthesis tasks. These results highlight its effectiveness in computational biosynthesis and its potential to facilitate the design of NP-based therapeutics.
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