Articlenpj antimicrobials and resistance2026
Accelerating natural product discovery with linked MS-genomics and language/transformer-based models.
Article in npj antimicrobials and resistance, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Combating Antibacterial Resistance: The Integrative Role of Artificial Intelligence in Bio-Based Product Development.Antibiotics (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Integrated chem-bio characterization of microbial strain libraries can streamline natural product discovery by prioritizing candidate producers. Here, we employ language- and transformer-based models to extract actionable insights from linked mass spectrometry (MS)-genome datasets. Our framework enables ranking of microbial producers to prioritise high-potential candidates for targeted validation. Across three representative case studies, this approach prioritized producers of diverse natural products with 75-100% precision. These findings demonstrate the transformative potential of AI-enabled chem-bio characterization to significantly accelerate natural product discovery and enable access to microbial chemical diversity beyond reference knowledge.
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.