ArticleJournal of natural products2025
Machine Learning-Based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics.
Article in Journal of natural products, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Integrating Artificial Intelligence Into Drug Discovery From Medicinal Plants: Current Applications and Infrastructural Challenges.Chemical biology & drug design · 2026Review
- The Computational Revolution in Natural Product Research: A Data-Driven Roadmap for Next-Generation Drug Development.Biology · 2026Review
- Molecular networking, conformal predictions and revised fingerprint-based models for discovering endocrine disruptors in mixtures.Analytical and bioanalytical chemistry · 2026Article
- Identification of Deoxy- andJournal of natural products · 2026Article
- Productive chaos and precision engineering: decoupling discovery from manufacturing to revolutionize plant-inspired therapeutics.Frontiers in plant science · 2026Article
- AnnoMe: user-defined classification of HR-MS/MS spectra for natural product discovery.Bioinformatics advances · 2026Article
- Molecules to medicine: advances in metabolomics for natural product drug discovery.Current opinion in biotechnology · 2025Review
- Review
- Targeted Isolation of Prenylated Flavonoids fromMetabolites · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The rediscovery of known drug classes represents a major challenge in natural products drug discovery. Compound rediscovery inhibits the ability of researchers to explore novel natural products and wastes significant amounts of time and resources. This study introduces a novel machine learning framework that can effectively characterize the bioactivity of natural products by leveraging liquid chromatography tandem mass spectrometry and untargeted metabolomics analysis. This accelerates natural product drug discovery by addressing the challenge of dereplicating previously discovered bioactive compounds. Utilizing the SIRIUS 5 metabolomics software suite and
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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.