ArticleScience advances2025
Automated annotation of complex natural products using a modular fragmentation-based structure assembly (MFSA) strategy.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- DeepMASS v.2: An enhanced deep learning platform for large-scale discovery and structural annotation of unknown plant metabolites.Plant communications · 2026Article
- Liquid chromatography-mass spectrometry-based automated annotation program-assisted structural characterization of daphnane diterpenoids from Daphne genkwa.Journal of natural medicines · 2026Article
- Identification of Daphnane Diterpenoids from Flower Buds and Blooming Flowers ofPlants (Basel, Switzerland) · 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
No grant is acknowledged in the PubMed record.
Abstract
Complex natural products (CNPs) feature polycyclic structures, multiple consecutive chiral centers, and nonrepetitive structural units. Given their complexity, the structural annotation of CNPs remains a major bottleneck. Here, we introduce the modular fragmentation-based structural assembly (MFSA) strategy for the target CNP structural annotation. The MFSA strategy disassembled the structure based on fragmentation patterns, recognized targets via a pseudo-library, and reassembled the structure using characteristic ions and neutral losses. As a proof of concept, we focused on daphnane-type diterpenoids, a kind of specific bioactive CNP from Thymelaeaceae plants. Furthermore, we present a user-friendly application named CNPs-MFSA coded in Python. Using an in-house daphnane library, CNPs-MFSA outperformed SIRIUS, MS-FINDER, and MetFrag in Top-1 annotation accuracy. By applying CNPs-MFSA to 56 Thymelaeaceae plants, 204 high-confidence daphnanes within 822 annotated results, including 105 previously unreported compounds were found. Aconitine, paclitaxel, and obakunone analogs were incorporated as additional target CNP classes to illustrate the extension workflow.
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.