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DOI: https://doi.org/10.6023/cjoc202505022

综述与进展

机器学习设计有机荧光诊疗分子的研究进展

  • 郭浩哲 ,
  • 李玉银 ,
  • 汤培琛 ,
  • 樊江莉
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  • a大连理工大学精细化工国家重点实验室 辽宁 大连 116024;
    b大连理工大学宁波研究院 浙江 宁波 315016

收稿日期: 2025-05-20

  修回日期: 2025-06-05

  网络出版日期: 2025-07-18

基金资助

本文受国家留学基金 (No.202406060052). 国家自然科学基金项目(22338005, 22494701); 辽宁滨海实验室基金(LBLB-2023-03); 中央高校基本科研业务费(DUT22LAB601); 宁波自然科学基金(2024Z218) 资助.

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Advances in Machine Learning-Based Design of Organic Fluorescent Theranostic Molecules

  • Guo Haozhe ,
  • Li Yuyin ,
  • Tang Peichen ,
  • Fan Jiangli
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  • aState Key Laboratory of Fine Chemicals, Frontier Science Center for Smart Materials, Dalian University of Technology, Dalian 116024, China;
    bNingbo Institute of Dalian University of Technology, Ningbo 315016, China.

Received date: 2025-05-20

  Revised date: 2025-06-05

  Online published: 2025-07-18

Supported by

China Scholarship Council (No.202406060052), National Natural Science Foundation of China (22338005, 22494701), Liaoning Binhai Laboratory (LBLB-2023-03), Fundamental Research Funds for the Central Universities (DUT22LAB601) and Natural Science Foundation of Ningbo Municipality:2024Z218.

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摘要

有机荧光诊疗分子因其高灵敏度、优异的生物相容性、低毒性、诊疗一体化等特点广泛应用于生命成像与肿瘤治疗等领域. 然而, 随着精细化诊疗需求的不断增加, 传统的分子设计方法受限于长周期试错实验与高昂计算成本, 难以满足设计需求. 基于机器学习(Machine Learning, ML)方法直接构建有机分子各种性质与结构的映射关系成为荧光分子设计领域有效提高精准诊疗功能、缩短设计周期的新方法. 本文系统梳理了基于各种ML算法的荧光分子设计模型, 针对多种诊疗分子特征属性对当前研究进行归类综述, 并提出了未来基于ML方法分子设计的发展方向.

关键词： 机器学习; 荧光诊疗分子; 分子设计; 构效关系; 分子描述符; 数据库

本文引用格式

郭浩哲 , 李玉银 , 汤培琛 , 樊江莉 . 机器学习设计有机荧光诊疗分子的研究进展[J]. 有机化学, 0 : 5022 . DOI: 10.6023/cjoc202505022

Abstract

Organic fluorescent theranostic molecules have been extensively applied in fields such as biomedical imaging and tumor therapy, attributed to their remarkable features including high sensitivity, excellent biocompatibility, low toxicity, and integrated diagnosis - treatment capabilities. However, as the demand for precision theranostics continues to escalate, traditional molecular design strategies—limited by time-consuming trial-and-error experimentation and prohibitive computational costs—have become insufficient to meet contemporary requirements. Machine learning (ML) has emerged as a revolutionary approach in fluorescent molecule design by directly establishing structure-property relationships, thereby improving the accuracy of theranostic functions and drastically reducing the design cycle. This review systematically collates and analyzes the fluorescent molecule design models based on diverse ML algorithms. It categorizes and comprehensively summarizes the current research by focusing on multiple characteristic attributes of theranostic molecules. Moreover, it puts forward the future development directions of molecular design based on ML methods.

Key words： Machine learning; Fluorescent theranostic molecules; Molecular design; Structure-activity relationship; Molecular descriptors; Database.

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