Evidence map›Paper›PMID 40271040›Full record

ArticleChemical science2025

Enhancing fluorescent probe design through multilayer interaction convolutional networks: advancing biosensing and bioimaging precision.

Gongcheng Ma, Qihang Ding, Yuding Zhang, Xiaodong Zeng, Kai Zhu, Hongli Chen, Wenxuan Zhang, Qingzhi Wang, Shuman Huang, Ping Gong and 2 more

Abstract read
In one paragraph

Article in Chemical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review: structural determinants of organic dyes for controlling biological clearance pathways.Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Gongcheng MaSchool of Life Science and Technology, Xinxiang Medical University Xinxiang 453003 China.
Qihang DingDepartment of Chemistry, Korea University Seoul 02841 Korea dingqihang@whu.edu.cn.ORCID https://orcid.org/0000-0002-2665-9036
Yuding ZhangGuangdong Key Laboratory of Nanomedicine, CAS-HK Joint Lab of Biomaterials, CAS Key Laboratory of Biomedical Imaging Science and System, Shenzhen Engineering Laboratory of Nanomedicine and Nanoformulations, CAS Key Lab for Health Informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences Shenzhen 518055 China ping.gong@siat.ac.cn.
Xiaodong ZengState Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences Shanghai 201203 China.ORCID https://orcid.org/0000-0002-1442-0538
Kai ZhuKey Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province, School of Computer and Information Engineering, Henan Normal University Xinxiang 453007 China xuzhengweihhu@outlook.com.
Hongli ChenSchool of Life Science and Technology, Xinxiang Medical University Xinxiang 453003 China.ORCID https://orcid.org/0000-0002-7975-2509
Wenxuan ZhangInstitute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College Beijing 100050 China.ORCID https://orcid.org/0000-0002-2046-3366
Qingzhi WangSchool of Life Science and Technology, Xinxiang Medical University Xinxiang 453003 China.
Shuman HuangKey Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province, School of Computer and Information Engineering, Henan Normal University Xinxiang 453007 China xuzhengweihhu@outlook.com.
Ping GongGuangdong Key Laboratory of Nanomedicine, CAS-HK Joint Lab of Biomaterials, CAS Key Laboratory of Biomedical Imaging Science and System, Shenzhen Engineering Laboratory of Nanomedicine and Nanoformulations, CAS Key Lab for Health Informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences Shenzhen 518055 China ping.gong@siat.ac.cn.ORCID https://orcid.org/0000-0002-6687-0565
Zhengwei XuKey Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province, School of Computer and Information Engineering, Henan Normal University Xinxiang 453007 China xuzhengweihhu@outlook.com.
Xuechuan HongDepartment of Cardiology, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University Wuhan 430071 China xhy78@whu.edu.cn.ORCID https://orcid.org/0000-0001-6834-3278

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fluorescent probes are pivotal in biosensing and bioimaging, necessitating precise spectral tailoring for high-performance applications. Despite their importance, probe design remains largely empirical, a process that is both time-consuming and laborious. To streamline this, we created a comprehensive dataset of over 600 rhodamine fluorescent probes and employed a multilayer interaction convolutional model (MICNet) trained on molecular fingerprints to accurately predict excitation and emission wavelengths. Our model demonstrated high accuracy with mean relative errors (MRE) of 0.1% for excitation and 0.4% for emission wavelengths. Advancing this, we implemented a closed-loop strategy that integrates experimental feedback to iteratively enhance the design algorithm's accuracy, thereby improving the probes' performance and reliability. This method not only accelerates the probe development cycle but also facilitates the creation of spectrally customized fluorescent probes, offering a significant advancement in the field of bioanalytical chemistry.

Identifiers

PMID40271040
PMCPMC12012562

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Registered trials

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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.