ArticleJournal of chemical information and modeling2026
BGC-MAC and BGC-MAP: Attention-Based Models for Biosynthetic Gene Cluster Classification and Product Matching.
Article in Journal of chemical information and modeling, 2026. 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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4 citing papers in PubMed.
- Fine-grained structural classification of biosynthetic gene cluster-encoded products.Bioinformatics (Oxford, England) · 2026Article
- A foundation model enables prediction of natural product molecular properties, bioactivity, and structural similarity from biosynthetic gene cluster sequence.bioRxiv : the preprint server for biology · 2026Article
- Unified genomic and chemical representations enable bidirectional biosynthetic gene cluster and natural product retrieval.Scientific reports · 2026Article
- ACP-EPC: an interpretable deep learning framework for anticancer peptide prediction utilizing pre-trained protein language model and multi-view feature extracting strategy.Molecular diversity · 2026Article
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Authors and funding
9 authors.
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Abstract
Natural products, synthesized via enzymes encoded by biosynthetic gene clusters (BGCs), represent a major source of therapeutic agents. Accurate BGC annotation is essential to unlocking the vast potential of natural product diversity. However, BGC annotation remains challenging due to our incomplete understanding of the enzymatic logic underlying biosynthesis. Here, we present two deep learning models trained on experimentally validated BGC-natural product pairs to advance BGC annotation. The BGC-multihead attention classifier (BGC-MAC) classifies BGCs by natural product class, outperforming antiSMASH and DeepBGC. The BGC-multihead attention product-matcher (BGC-MAP) associates BGCs with product structures, demonstrating potential to prioritize candidate BGCs given a natural product or to identify potential natural products from a given BGC. Importantly, the models' cross-attention mechanisms enable explainable AI, identifying key protein domains and revealing BGC-substructure relationships in the biosynthesis without requiring prior annotations. Together, BGC-MAC and BGC-MAP establish a data-driven, explainable AI framework that enhances BGC annotation, deepens biosynthetic insight, and accelerates the discovery of new natural products. The software is available at https://github.com/EvoCatalysis/BGC_annotation.
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