Accepted for/Published in: JMIR Bioinformatics and Biotechnology
Date Submitted: Jul 12, 2025
Open Peer Review Period: Jul 12, 2025 - Sep 6, 2025
Date Accepted: Oct 19, 2025
(closed for review but you can still tweet)
An integration of LC-MS and DeepSeek models to unearth compounds with wound-healing properties from Cayratia japonica exosome-like nanovesicles
ABSTRACT
Background:
Null
Objective:
To create a multimodal framework of liquid chromatography-mass spectrometry (LC-MS) combined with DeepSeek models for data mining of compounds with wound-healing properties from exosome-like nanovesicles derived from Cayratia japonica (CJ-ELNs).
Methods:
LC-MS identified compounds enriched in both CJ and CJ-ELNs via a four-step filtering workflow. The intersected compounds were processed by DeepSeek models for screening naturally active compounds with targeted functions of antioxidation, anti-inflammation, anti-cellular damage, anti-apoptosis, wound healing and tissue regeneration, and cell proliferation.
Results:
A multimodal framework of LC-MS combined with the DeepSeek-DF model was created. With the assistance of artificial intelligence (AI), a total of 47 naturally active compounds derived from CJ-ELNs with targeted functions were identified.
Conclusions:
A self-designed multimodal framework of LC-MS combined with DeepSeek models rapidly and accurately identifies naturally active compounds from CJ-ELNs. This AI-powered system innovatively integrates the traditional analytical technique with modern large language models, thus greatly favoring data mining of active ingredients in traditional Chinese medicine (TCM) herbs.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.