ArticleJournal of chemical information and modeling2021
A Machine Learning Bioinformatics Method to Predict Biological Activity from Biosynthetic Gene Clusters.
Article in Journal of chemical information and modeling, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
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Who cites it
52 citing papers in PubMed, 1 synthesis or guideline pooled it, 110 citations in OpenAlex.
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- Antibiotic-producing microorganisms: taxonomic diversity, screening strategies, and emerging and underexploited antibacterial targets.Archives of microbiology · 2026Review
- Large-scale discovery platform enables identification of peptides targeting drug-resistant candidiasis.Cell reports methods · 2026Article
- Siwa spring microbiomes as reservoirs of biosynthetic gene clusters: Unlocking natural product potential.World journal of microbiology & biotechnology · 2026Article
- Identification of Siderophores With Unexpected Antibacterial Properties From Actinoplanes teichomyceticus.Chembiochem : a European journal of chemical biology · 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
- Review
- Harnessing artificial intelligence for antimicrobial discovery and optimization.Current opinion in microbiology · 2026Review
- Repositioning Natural Products in Modern Drug Discovery: Technological Innovation, Systems Pharmacology, and Pathological Validation.International journal of molecular sciences · 2026Review
- AI-Assisted Isolation of Bioactive Dipyrimicins fromJournal of natural products · 2026Article
- Discovery of natural products that modulate signaling in patient-derived cells.RSC chemical biology · 2026Article
- Green genes from blue greens: challenges and solutions to unlocking the potential of cyanobacteria in drug discovery.Natural product reports · 2026Review
- Bridging ecological processes to elevated antibiotic resistance risk in tomato microbiome under fungicide stress.The ISME journal · 2026Article
- Article
- Mobilome-mediated transcriptional activation of biosynthetic gene clusters and its impact on strain competitiveness in food fermentation microbiomes.Microbiome · 2025Article
- AI-assisted isolation of bioactive Dipyrimicins frombioRxiv : the preprint server for biology · 2025Article
- CSEL-BGC: A Bioinformatics Framework Integrating Machine Learning for Defining the Biosynthetic Evolutionary Landscape of Uncharacterized Antibacterial Natural Products.Interdisciplinary sciences, computational life sciences · 2025Article
- Amphibian skin bacteria contain a wide repertoire of genes linked to their antifungal capacities.World journal of microbiology & biotechnology · 2025Article
- Artificial Intelligence in Natural Product Drug Discovery: Current Applications and Future Perspectives.Journal of medicinal chemistry · 2025Review
- Interpretable adenylation domain specificity prediction using protein language models.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Research in natural products, the genetically encoded small molecules produced by organisms in an idiosyncratic fashion, deals with molecular structure, biosynthesis, and biological activity. Bioinformatics analyses of microbial genomes can successfully reveal the genetic instructions, biosynthetic gene clusters, that produce many natural products. Genes to molecule predictions made on biosynthetic gene clusters have revealed many important new structures. There is no comparable method for genes to biological activity predictions. To address this missing pathway, we developed a machine learning bioinformatics method for predicting a natural product's antibiotic activity directly from the sequence of its biosynthetic gene cluster. We trained commonly used machine learning classifiers to predict antibacterial or antifungal activity based on features of known natural product biosynthetic gene clusters. We have identified classifiers that can attain accuracies as high as 80% and that have enabled the identification of biosynthetic enzymes and their corresponding molecular features that are associated with antibiotic activity.
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Registered trials
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.