ReviewChemical science2022
Natural product drug discovery in the artificial intelligence era.
Review in Chemical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 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
73 citing papers in PubMed.
- Breaking the antimicrobial resistance dilemma: marine natural products, a treasure trove for the development of new anti-Marine life science & technology · 2026Review
- Review
- Therapeutic Applications of Natural Products in Biomedicine and Pharmacotherapy.Life (Basel, Switzerland) · 2026Review
- The Computational Revolution in Natural Product Research: A Data-Driven Roadmap for Next-Generation Drug Development.Biology · 2026Review
- Nature meets machine: the AI renaissance in natural product drug discovery.Natural products and bioprospecting · 2026Review
- Review
- Rethinking Nature's Pharmacy: AI Era and Natural Product Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Mining the Hidden Pharmacopeia: Fungal Endophytes, Natural Products, and the Rise of AI-Driven Drug Discovery.International journal of molecular sciences · 2026Review
- Marine natural products.Natural product reports · 2026Review
- Artificial Intelligence for Natural Products Drug Discovery in Neurodegenerative Therapies: A Review.Biomolecules · 2026Review
- NPASS database update 2026: comprehensive quantitative composition, bioactivity, and ADME-Tox data of natural products for biomedical research.Nucleic acids research · 2026Article
- Multidimensional mechanisms of natural products in modulating metabolic dysfunction-associated kidney disease: recent advances and future perspectives.Frontiers in pharmacology · 2026Review
- Assessing biological activities of polyphenols in the context of 4R - challenges of a ubiquitous compound class with complex modes of action.Frontiers in pharmacology · 2026Review
- Green Drug Discovery: Leveraging Biodiversity for Sustainable Pharmaceutical Solutions.Chemistry & biodiversity · 2026Review
- Recent Advances in Boswellic Acids as Anticancer Agents: Therapeutic Effects, SAR, and Computational Approaches.Current topics in medicinal chemistry · 2026Review
- Rational Design of Chlorinated Hydroxyxanthone as Anticancer Agent: Molecular Docking, Molecular Dynamic Simulation, ADMET, One-Pot Synthesis, and In Vitro Evaluation.Cell biochemistry and biophysics · 2025Article
- NPBS Atlas: a comprehensive data resource for exploring the biological sources of natural products.Journal of cheminformatics · 2025Article
- Update and ADMET Profile of the Latin American Natural Product Database: LANaPDB.Molecular informatics · 2025Article
- Using Machine Learning for the Discovery and Development of Multitarget Flavonoid-Based Functional Products in MASLD.Molecules (Basel, Switzerland) · 2025Article
- Natural Products from Soft Corals in Taiwan: Insights into Chemical Taxonomy and Potential Interaction in Marine Environments.Marine biotechnology (New York, N.Y.) · 2025Review
13 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.
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.