机器学习设计有机荧光诊疗分子的研究进展★

doi:10.6023/cjoc20205022

有机化学 ›› 2025, Vol. 45 ›› Issue (9): 3203-3212.DOI: 10.6023/cjoc20205022 上一篇下一篇

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^a, 李玉银^a, 汤培琛^a, 樊江莉^a^,^b^,^*()

^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 发布日期:2025-07-17
基金资助:
国家留学基金(202406060052); 国家自然科学基金(22338005); 国家自然科学基金(22494701); 辽宁滨海实验室(LBLB-2023-03); 中央高校基本科研业务费(DUT22LAB601); 宁波自然科学基金(2024Z218)

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

Haozhe Guo^a, Yuyin Li^a, Peichen Tang^a, Jiangli Fan^a^,^b^,^*()

^a State Key Laboratory of Fine Chemicals, Frontier Science Center for Smart Materials, Dalian University of Technology, Dalian, Liaoning 116024
^b Ningbo Institute of Dalian University of Technology, Ningbo, Zhejiang 315016

Received:2025-05-20 Revised:2025-06-05 Published:2025-07-17
Contact: *E-mail: fanjl@dlut.edu.cn
About author:
^★ Academic Papers of the 27^th Annual Meeting of the China Association for Science and Technology.
Supported by:
China Scholarship Council(202406060052); National Natural Science Foundation of China(22338005); National Natural Science Foundation of China(22494701); Liaoning Binhai Laboratory(LBLB-2023-03); Fundamental Research Funds for the Central Universities(DUT22LAB601); Natural Science Foundation of Ningbo Municipality(2024Z218)

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摘要/Abstract

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. 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

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. The fluorescent molecule design models are systematically collated and analyzed based on diverse ML algorithms. The current research is categorized and comprehensively summarized by focusing on multiple characteristic attributes of theranostic molecules. Moreover, the future development directions of molecular design based on ML methods has been proposed.

Key words: machine learning, fluorescent theranostic molecules, molecular design, structure-activity relationship, molecular descriptors, database

引用此文

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, 2025, 45(9): 3203-3212.

Haozhe Guo, Yuyin Li, Peichen Tang, Jiangli Fan. Advances in Machine Learning-Based Design of Organic Fluorescent Theranostic Molecules^★[J]. Chinese Journal of Organic Chemistry, 2025, 45(9): 3203-3212.

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Structure-function relationship	Material category	Theranostic Attribute	Algorithm	Advantage
Membrane perme- ability descriptor	BODIPY, etc.	Bioimaging	D-MPNN, DNN, B-PvsC, HTS, etc.	Increase of membrane permeability and biocompatibility
AIE/ACQ	Molecules with extended conjugation	Bioimaging, theranostic of multiple diseases	SVM, DNN, TD-DFT, etc.	Acceleration of the molecule discovery cycle, proposal of new mechanisms
ΔE_HL	Near-infrared fluore- scent molecules	Deep-tissue bioimaging	GCNN, ETR, XGBoost, etc.	Increase of molecular backbone stability
pH	Oxazene, etc.	Deep-tissue bioimaging	ATTRNN, TD-DFT, etc.	PH-regulated fluorescence switching, disease-specific imaging
ISC	PS, TADF, RTP, etc.	PDT, Bioimaging	BO, VAE, HIPNN, etc.	Prolongation of material triplet-state lifetime, acceleration of materials discovery, Enhancement of precision theranostic performance
PTT	PTA, etc.	PTT, PAI	RF, GAN, GA, etc.	Reduction of the photothermal agent deve- lopment cycle, increase of photothermal efficiency

Structure-function relationship	Material category	Theranostic Attribute	Algorithm	Advantage
Membrane perme- ability descriptor	BODIPY, etc.	Bioimaging	D-MPNN, DNN, B-PvsC, HTS, etc.	Increase of membrane permeability and biocompatibility
AIE/ACQ	Molecules with extended conjugation	Bioimaging, theranostic of multiple diseases	SVM, DNN, TD-DFT, etc.	Acceleration of the molecule discovery cycle, proposal of new mechanisms
ΔE_HL	Near-infrared fluore- scent molecules	Deep-tissue bioimaging	GCNN, ETR, XGBoost, etc.	Increase of molecular backbone stability
pH	Oxazene, etc.	Deep-tissue bioimaging	ATTRNN, TD-DFT, etc.	PH-regulated fluorescence switching, disease-specific imaging
ISC	PS, TADF, RTP, etc.	PDT, Bioimaging	BO, VAE, HIPNN, etc.	Prolongation of material triplet-state lifetime, acceleration of materials discovery, Enhancement of precision theranostic performance
PTT	PTA, etc.	PTT, PAI	RF, GAN, GA, etc.	Reduction of the photothermal agent deve- lopment cycle, increase of photothermal efficiency

机器学习设计有机荧光诊疗分子的研究进展^★

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

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机器学习设计有机荧光诊疗分子的研究进展★

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

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机器学习设计有机荧光诊疗分子的研究进展^★

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