ArticleNature chemistry2014
Revealing the macromolecular targets of complex natural products.
Article in Nature chemistry, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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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.
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Who cites it
43 citing papers in PubMed, 131 citations in OpenAlex.
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- Mining the Hidden Pharmacopeia: Fungal Endophytes, Natural Products, and the Rise of AI-Driven Drug Discovery.International journal of molecular sciences · 2026Review
- Integrative proteome-wide structural analysis and high-throughput docking identify broad-spectrum antiviral scaffolds against Zika, Yellow Fever, West Nile, Saint Louis encephalitis, and Usutu viruses.Frontiers in cellular and infection microbiology · 2026Article
- AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential.NPJ digital medicine · 2025Review
- Artificial Intelligence in Natural Product Drug Discovery: Current Applications and Future Perspectives.Journal of medicinal chemistry · 2025Review
- Artificial intelligence for natural product drug discovery.Nature reviews. Drug discovery · 2023Review
- The rise of automated curiosity-driven discoveries in chemistry.Chemical science · 2023Review
- TCMSID: a simplified integrated database for drug discovery from traditional chinese medicine.Journal of cheminformatics · 2022Article
- Language models can learn complex molecular distributions.Nature communications · 2022Article
- Natural product drug discovery in the artificial intelligence era.Chemical science · 2022Review
- Computer-Aided Drug Design (CADD) to De-Orphanize Marine Molecules: Finding Potential Therapeutic Agents for Neurodegenerative and Cardiovascular Diseases.Marine drugs · 2022Article
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- Towards Advances in Medicinal Plant Antimicrobial Activity: A Review Study on Challenges and Future Perspectives.Microorganisms · 2021Review
- Mining genomes to illuminate the specialized chemistry of life.Nature reviews. Genetics · 2021Review
- Learning from Nature: From a Marine Natural Product to Synthetic Cyclooxygenase-1 Inhibitors by Automated De Novo Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Article
- Allosteric Antagonist Modulation of TRPV2 by Piperlongumine Impairs Glioblastoma Progression.ACS central science · 2021Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Cheminformatics in Natural Product-based Drug Discovery.Molecular informatics · 2020Review
Corrections and comments
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Authors and funding
14 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Natural products have long been a source of useful biological activity for the development of new drugs. Their macromolecular targets are, however, largely unknown, which hampers rational drug design and optimization. Here we present the development and experimental validation of a computational method for the discovery of such targets. The technique does not require three-dimensional target models and may be applied to structurally complex natural products. The algorithm dissects the natural products into fragments and infers potential pharmacological targets by comparing the fragments to synthetic reference drugs with known targets. We demonstrate that this approach results in confident predictions. In a prospective validation, we show that fragments of the potent antitumour agent archazolid A, a macrolide from the myxobacterium Archangium gephyra, contain relevant information regarding its polypharmacology. Biochemical and biophysical evaluation confirmed the predictions. The results obtained corroborate the practical applicability of the computational approach to natural product 'de-orphaning'.
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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.