ReviewJournal of medicinal chemistry2025
Artificial Intelligence in Natural Product Drug Discovery: Current Applications and Future Perspectives.
Review in Journal of medicinal chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 44 papers, 1 of them a synthesis that pooled it.
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
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Theoretical visualization of covalent and non-covalent interactions: molecular diversity and substituent effects governing the reactivity of Bulbophyllum bibenzyl derivatives.Molecular diversity · 2026Article
- Biological applications and future perspectives of bioactive compounds as pig feed additives.Animal nutrition (Zhongguo xu mu shou yi xue hui) · 2026Review
- Few-shot learning-driven discovery of Lutein suppresses Th1-mediated inflammation via glucose metabolism.Acta pharmacologica Sinica · 2026Article
- Dual enzymatic inhibition and toxicity profiling of polymethoxylated flavones isolated from Knema malayana.Journal of computer-aided molecular design · 2026Article
- Generative AI-drivenAntibody therapeutics · 2026Article
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- Comprehensive Insights into Natural Bioactive Compounds: From Chemical Diversity and Mechanisms to Biotechnological Innovations and Applications.ChemistryOpen · 2026Review
- Antimicrobial Effect of Spices and Their Phytochemicals: A Novel Approach to Overcoming Antibiotic Resistance.MedComm · 2026Review
- Nature meets machine: the AI renaissance in natural product drug discovery.Natural products and bioprospecting · 2026Review
- Research advances and future perspectives of nanomaterials for depression therapy: a scientometric review.Journal of nanobiotechnology · 2026Review
- Cycloastragenol Derivatives Improve Tyrosine Metabolism, Regulate TLR4/NF-κB/TERT Signaling Pathways, and Inhibit MPTP Induced Neuroinflammation and PD Symptoms.CNS neuroscience & therapeutics · 2026Article
- Marine natural products.Natural product reports · 2026Review
- AlternativeExpert reviews in molecular medicine · 2026Review
- BEDB: a comprehensive binding energy database for molecular docking and dynamics: insights into Human Metapneumovirus (HMPV) Inhibitors.Database : the journal of biological databases and curation · 2026Article
- Targeting Bacterial Cell Wall Synthesis: Structural Insights and Emerging Therapeutic Strategies.Pharmaceutics · 2026Review
- Advances in natural product discovery: strategies, technologies, and insights.Natural products and bioprospecting · 2026Review
- NPASS database update 2026: comprehensive quantitative composition, bioactivity, and ADME-Tox data of natural products for biomedical research.Nucleic acids research · 2026Article
- TRAIL of Hope: Bioactive Dietary Component-Driven Carrier-Free Nanoplatforms for Synergistically Enhanced Tumor Multimodal Therapy.International journal of nanomedicine · 2026Review
- AI-driven drug discovery using transformer-based molecular representation learning.Frontiers in artificial intelligence · 2026Article
Corrections and comments
- Erratum issued
- Retracted
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug discovery, a multifaceted process from compound identification to regulatory approval, historically plagued by inefficiencies and time lags due to limited data utilization, now faces urgent demands for accelerated lead compound identification. Innovations in biological data and computational chemistry have spurred a shift from trial-and-error methods to holistic approaches to medicinal chemistry. Computational techniques, particularly artificial intelligence (AI), notably machine learning (ML) and deep learning (DL), have revolutionized drug development, enhancing data analysis and predictive modeling. Natural products (NPs) have long served as rich sources of biologically active compounds, with many successful drugs originating from them. Advances in information science expanded NP-related databases, enabling deeper exploration with AI. Integrating AI into NP drug discovery promises accelerated discoveries, leveraging AI's analytical prowess, including generative AI for data synthesis. This perspective illuminates AI's current landscape in NP drug discovery, addressing strengths, limitations, and future trajectories to advance this vital research domain.
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.