ArticlePLoS computational biology2023
Deep self-supervised learning for biosynthetic gene cluster detection and product classification.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 38 citations in OpenAlex.
- Fine-grained structural classification of biosynthetic gene cluster-encoded products.Bioinformatics (Oxford, England) · 2026Article
- Regulatory and metabolic control of microbial biosynthetic gene clusters.Communications biology · 2026Review
- 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
- A review of computational approaches for metagenomics by long-read sequencing.Science China. Life sciences · 2026Review
- Harnessing artificial intelligence for antimicrobial discovery and optimization.Current opinion in microbiology · 2026Review
- ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein-protein interactions and gene essentiality across taxa.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Unified genomic and chemical representations enable bidirectional biosynthetic gene cluster and natural product retrieval.Scientific reports · 2026Article
- Combating Antibacterial Resistance: The Integrative Role of Artificial Intelligence in Bio-Based Product Development.Antibiotics (Basel, Switzerland) · 2026Review
- BGC-MAC and BGC-MAP: Attention-Based Models for Biosynthetic Gene Cluster Classification and Product Matching.Journal of chemical information and modeling · 2026Article
- Advances in natural product discovery: strategies, technologies, and insights.Natural products and bioprospecting · 2026Review
- Biodiversity-Driven Natural Products and Bioactive Metabolites.Plants (Basel, Switzerland) · 2025Review
- Benchmarking methods for measuring biosynthetic gene cluster similarity and determination of gene cluster families.Bioinformatics (Oxford, England) · 2025Article
- Sequence modeling tools to decode the biosynthetic diversity of the human microbiome.mSystems · 2025Review
- Deciphering the biosynthetic potential of microbial genomes using a BGC language processing neural network model.Nucleic acids research · 2025Article
- Synthetic Biology in Natural Product Biosynthesis.Chemical reviews · 2025Review
- Exploring the secrets of marine microorganisms: Unveiling secondary metabolites through metagenomics.Microbial biotechnology · 2024Review
- Recent advances in deep learning and language models for studying the microbiome.Frontiers in genetics · 2024Review
- Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products.ACS synthetic biology · 2023Review
- From sequence to function through structure: Deep learning for protein design.Computational and structural biotechnology journal · 2023Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Natural products are chemical compounds that form the basis of many therapeutics used in the pharmaceutical industry. In microbes, natural products are synthesized by groups of colocalized genes called biosynthetic gene clusters (BGCs). With advances in high-throughput sequencing, there has been an increase of complete microbial isolate genomes and metagenomes, from which a vast number of BGCs are undiscovered. Here, we introduce a self-supervised learning approach designed to identify and characterize BGCs from such data. To do this, we represent BGCs as chains of functional protein domains and train a masked language model on these domains. We assess the ability of our approach to detect BGCs and characterize BGC properties in bacterial genomes. We also demonstrate that our model can learn meaningful representations of BGCs and their constituent domains, detect BGCs in microbial genomes, and predict BGC product classes. These results highlight self-supervised neural networks as a promising framework for improving BGC prediction and classification.
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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.