ArticleNature chemical biology2015
Computational approaches to natural product discovery.
Article in Nature chemical biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 267 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
267 citing papers in PubMed.
- From predicted bacteriocins to ecological function.Current research in microbial sciences · 2026Review
- Accelerating natural product discovery, characterization and engineering by biofoundries.Natural product reports · 2026Review
- Artificial intelligence-driven advancements in agricultural biotechnology.Journal, genetic engineering & biotechnology · 2026Review
- Tandem-sgRNA Provides an Effective Reverse Genetic Approach for Suppression of Streptomyces Biosynthetic Gene Clusters and Secondary Metabolism.ACS synthetic biology · 2026Article
- Marine-Derived Rare Actinomycetes: Metabolites and Their Biosynthesis.Marine drugs · 2026Review
- Discovery of a phenazine-thiol conjugase from sparse data using genome-informed machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Exploring halophilic microorganisms for novel antimicrobial discovery: molecular mechanisms and computational approaches.Folia microbiologica · 2026Review
- Orsellinic Acid from the EndophyticACS omega · 2026Article
- Regulatory and metabolic control of microbial biosynthetic gene clusters.Communications biology · 2026Review
- DiscERN: an automated genome mining tool for the discovery of evolutionarily related natural products.Nature communications · 2026Article
- Natural Products as Promising Pharmacological Agents Against Cancer: A Holistic Overview of Their Anti-Cancer Mechanisms of Action of the Last Five Years.Pharmaceuticals (Basel, Switzerland) · 2026Review
- An indole dimer antifungal metabolite from a rice seed endophyte inhibits ergosterol biosynthesis in fungal pathogens.Applied and environmental microbiology · 2026Article
- Harnessing engineered cyanobacteria for next-generation therapeutics.Archives of microbiology · 2026Review
- Discovery of a phenazine-thiol conjugase from sparse data using genome-informed machine learning.bioRxiv : the preprint server for biology · 2026Article
- Refining the Study of Endophytic Fungi: Best Practices in Antibacterial Research.Current microbiology · 2026Review
- BiG-SCAPE 2.0 and BiG-SLiCE 2.0: scalable, accurate and interactive sequence clustering of metabolic gene clusters.Nature communications · 2026Article
- Synthetic Biology Strategies for Activating Cryptic BGCs inACS omega · 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
- The antiSMASH database version 5.Nucleic acids research · 2026Article
207 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Starting with the earliest Streptomyces genome sequences, the promise of natural product genome mining has been captivating: genomics and bioinformatics would transform compound discovery from an ad hoc pursuit to a high-throughput endeavor. Until recently, however, genome mining has advanced natural product discovery only modestly. Here, we argue that the development of algorithms to mine the continuously increasing amounts of (meta)genomic data will enable the promise of genome mining to be realized. We review computational strategies that have been developed to identify biosynthetic gene clusters in genome sequences and predict the chemical structures of their products. We then discuss networking strategies that can systematize large volumes of genetic and chemical data and connect genomic information to metabolomic and phenotypic data. Finally, we provide a vision of what natural product discovery might look like in the future, specifically considering longstanding questions in microbial ecology regarding the roles of metabolites in interspecies interactions.
Indexed as
Identifiers
What OpenQuestion holds
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.