中草药

柔性-环增强图神经网络用于中药分子复杂结构的性质预测

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贺怀 ¹, , 陈治瑜 ², , 任琪 ², , 陈双扣 ¹, , 钟节 ², , 陶梦瑶 ², , 程榆淞 ², , 代浚豪 ², , 周寰宇 ², , 刘曾 ², , 戴传云 ²,

中草药 | 化学成分 2026,57(16): 6260-6272

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中草药 |化学成分 2026 , 57 (16) : 6260 -6272

柔性-环增强图神经网络用于中药分子复杂结构的性质预测

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贺怀¹,, 陈治瑜²,, 任琪²,, 陈双扣¹,, 钟节²,, 陶梦瑶²,, 程榆淞²,, 代浚豪²,, 周寰宇²,, 刘曾²,, 戴传云²,

作者信息

^1.重庆科技大学化学化工学院, 重庆 401331

^2.重庆中医药学院中药学院, 制药过程数字化重庆市重点实验室, 重庆 402760

^3.重庆科技大学化学化工学院, 重庆 401331

^4.重庆中医药学院中药学院, 制药过程数字化重庆市重点实验室, 重庆 402760

通讯作者:

陈双扣

作者简介:

贺怀: 贺怀(1999—),男,硕士研究生,研究方向为化学场景下的人工智能应用。E-mail:2727889123@qq.com

Property prediction of complex traditional Chinese medicine molecular structures using a flexibility-ring enhanced graph neural network

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出版时间: 2026-08-28 doi: 10.7501/j.issn.0253-2670.2026.16.005

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目的针对中药（traditional Chinese medicine，TCM）分子结构复杂、环系结构多样等特点，优化图同构神经网络（graph isomorphism network，GIN），提升其对中药分子关键性质的预测能力。方法基于GIN模型，在分子图中引入表示键长张力和角度张力的边描述符，并结合多个优势构象构建多图数据，从而构建了一种柔性-环增强图神经网络（flexibility-ring enhanced graph neural network，FRGNN）。在包含37 822个分子的2个中药数据库中，评估了该模型在7项关键分子属性上的预测性能，并与3种当前最优（state-of-the-art，SOTA）图神经网络模型及2种基础图神经网络模型进行了对比。结果与表现次优的模型相比，FRGNN在7项性质预测上的均方根误差平均降低了8.63%；对于含多环及大环结构的分子，其预测均方根误差进一步降低10.04%。结论 FRGNN模型在中药分子关键性质预测任务中表现出优于现有小分子预测SOTA模型的性能，为复杂天然化合物的性质预测提供了新的有效方法。

关键词

人工智能 / 图神经网络 / 中药分子 / 中药数据库 / 性质预测

Abstract

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Objective To address the complex molecular structures and diverse ring systems characteristic of traditional Chinese medicine (TCM) compounds, this study aims to optimize the graph isomorphism network (GIN) to enhance its predictive capability for key molecular properties of TCM molecules. Methods Based on the GIN architecture, edge descriptors representing bond length strain and angle strain were introduced into the molecular graph, and multi-graph data were constructed by incorporating multiple favorable conformations, thereby developing a flexibility-ring enhanced graph neural network (FRGNN). The predictive performance of the proposed model on seven key molecular properties was evaluated using two TCM databases containing 37 822 molecules, and compared against three state-of-the-art (SOTA) graph neural network models and two basic graph neural network models. Results Compared with the second-best performing model, FRGNN achieved an average reduction of 8.63% in root mean square error across the seven property prediction tasks, for molecules containing polycyclic and macrocyclic structures, the predictive root mean square error was further reduced by 10.04%. Conclusion The proposed FRGNN model demonstrates superior performance over existing state-of-the-art models for small-molecule property prediction in the task of predicting key properties of TCM compounds, providing a novel and effective approach for the property prediction of complex natural products.

Key words

artificial intelligence / graph neural networks / traditional Chinese medicine molecules / Chinese medicine datasets / property prediction

引用本文

贺怀, 陈治瑜, 任琪, 陈双扣, 钟节, 陶梦瑶, 程榆淞, 代浚豪, 周寰宇, 刘曾, 戴传云. 柔性-环增强图神经网络用于中药分子复杂结构的性质预测. 中草药, 2026 , 57 (16) : 6260 -6272 . DOI: 10.7501/j.issn.0253-2670.2026.16.005

HE Huai, CHEN Zhiyu, REN Qi, CHEN Shuangkou, ZHONG Jie, TAO Mengyao, CHENG Yusong, DAI Junhao, ZHOU Huanyu, LIU Zeng, DAI Chuanyun. Property prediction of complex traditional Chinese medicine molecular structures using a flexibility-ring enhanced graph neural network[J]. Chinese Traditional and Herbal Drugs, 2026 , 57 (16) : 6260 -6272 . DOI: 10.7501/j.issn.0253-2670.2026.16.005

基金

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国家自然科学基金面上项目
重庆市璧山区中医药领航联合专项重大项目
重庆市自然科学基金创新发展联合基金

参考文献

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王帅. 基于液相色谱技术的中药制剂质量控制标准研究[J]. 品牌与标准化, 2025(5): 133-135.

巩亦萍, 顾志荣, 郭引兄, 等. 中药药效物质基础研究方法与思路的进展与展望[J]. 中成药, 2025, 47(4): 1211-1216.

Votkina D, Petunin P, Miliutina E, et al. Uncovering the role of chemical and electronic structures in plasmonic catalysis: The case of homolysis of alkoxyamines [J]. ACS Catal, 2023, 13(5): 2822-2833.

Burés J, Armstrong A, Blackmond D G. Explaining anomalies in enamine catalysis: “Downstream species” as a new paradigm for stereocontrol [J]. Acc Chem Res, 2016, 49(2): 214-222.

Richter W E. The increased Diels-Alder reactivity of umpolung tropone: Analysis of individual atoms and bonds using QTAIM and IQA along complete IRC paths [J]. J Phys Org Chem, 2024, 37(7): e4572.

Hui B Y K, Le Osmund Chin K, Lim J J L, et al. Strategies to enhance the electrochromic properties of conjugated polymers bearing pyromellitic diimide acceptors [J]. Chem, 2024, 19(12): e202400236.

Ree N, Wollschläger J M, Göller A H, et al. Atom-based machine learning for estimating nucleophilicity and electrophilicity with applications to retrosynthesis and chemical stability [J]. Chem Sci, 2025, 16(13): 5676-5687.

Ghidini A, Serra E, et al. On free energy calculations in drug discovery [J]. Acc Chem Res, 2025, 58(20): 3137-3145.

Kim G, Grams R J, et al. Advancing covalent ligand and drug discovery beyond cysteine [J]. Chem Rev, 2025, 125(14): 6653-6684.

Gaudry A, Pagni M, Mehl F, et al. A sample-centric and knowledge-driven computational framework for natural products drug discovery [J]. ACS Cent Sci, 2024, 10(3): 494-510.

Adediwura V A, Koirala K, Do H N, et al. Understanding the impact of binding free energy and kinetics calculations in modern drug discovery [J]. Expert Opin Drug Discov, 2024, 19(6): 671-682.

Qian R T, Xue J, Xu Y, et al. Alchemical transformations and beyond: Recent advances and real-world applications of free energy calculations in drug discovery [J]. J Chem Inf Model, 2024, 64(19): 7214-7237.

Boulougouri M, Vandergheynst P, Probst D. Molecular set representation learning [J]. Nat Mach Intell, 2024, 6(7): 754-763.

Corso G, Stark H, Jegelka S, et al. Graph neural networks [J]. Nat Rev Meth Primers, 2024, 4: 17.

Xu K, Hu W, Leskovec J, et al. How powerful are graph neural networks? [A] // 7th International Conference on Learning Representations [C]. New Orleans: ICLR, 2019: 1-17.

Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks [A] // Proceedings of the 5th International Conference on Learning Representations [C]. Toulon: ICLR, 2017:1-14.

Fang X M, Liu L H, Lei J Q, et al. Geometry-enhanced molecular representation learning for property prediction [J]. Nat Mach Intell, 2022, 4(2):127-134.

Li Y S, Wang W, Liu J, et al. Pre-training molecular representation model with spatial geometry for property prediction [J]. Comput Biol Chem, 2024, 109: 108023.

Wu Z Q, Ramsundar B, Feinberg E N, et al. MoleculeNet: A benchmark for molecular machine learning [J]. Chem Sci, 2017, 9:513-530.

Chupakhin E, Babich O, Prosekov A, et al. Spirocyclic motifs in natural products [J]. Molecules, 2019, 24(22): 4165.

Nakase I, Lai H, Singh N P, et al. Anticancer properties of artemisinin derivatives and their targeted delivery by transferrin conjugation [J]. Int J Pharm, 2008, 354(1/2): 28-33.

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Zhu J H, Wu K, Wang B H, et al. 𝒪-GNN:Incorporating ring priors into molecular modeling [A] // Proceedings of the 11th International Conference on Learning Representations [C]. Kigali: ICLR, 2023: 1-19:

Qiao J B, Jin J R, Wang D, et al. A self-conformation-aware pre-training framework for molecular property prediction with substructure interpretability [J]. Nat Commun, 2025, 16: 4382.

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Axelrod S, Gómez-Bombarelli R.Molecular machine learning with conformer ensembles [J]. Mach Learn Sci Technol, 2023, doi:10.1088/2632-2153/acefa7.

Ru J L, Li P, Wang J N, et al. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines [J]. J Cheminf, 2014, 6(1): 13.

Gao K, et al. HERB 2.0: An updated database integrating clinical and experimental evidence for traditional Chinese medicine [J]. Nucleic Acids Res, 2025, 53(D1): D1404-D1414.

Ramsundar B, Eastman P, Walters P, et al. Deep learning for the life sciences: Applying deep learning to genomics, microscopy, drug discovery, and more [M]. Sebastopol: O'Reilly Media, 2019: 39-58.

Weininger D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules [J]. J Chem Inf Comput Sci, 1988, 28: 31-36.

Zhu Z, Li L, Wang Z, et al. PGL: A graph learning library for large-scale heterogeneous graphs [A] // Proceedings of the 30th ACM International Conference on Information & Knowledge Management [C]. New York: ACM, 2021: 4884-4888.

Ba J L, Kiros J R, Hinton G E. Layer normalization [EB/OL]. (2016-07-21) [2026-02-10]. https://arxiv.org/ abs/1607.06450.

Chen Y H, Tang X, Qi X B, et al. Learning graph normalization for graph neural networks [EB/OL]. (2020-09-24) [2026-02-10]. https://arxiv.org/abs/2009.11746.

Kingma D P, Ba J. Adam: A method for stochastic optimization [EB/OL]. (2014-12-22) [2026-02-10]. https://arxiv.org/abs/1412.6980.

Croitoru F A, Ristea N C, Ionescu R T, et al. Learning rate curriculum [J]. Int J Comput Vis, 2025, 133(1): 291-314.

Li L L, Zhang Y P, Wang G H, et al. Kolmogorov-Arnold graph neural networks for molecular property prediction [J]. Nat Mach Intell, 2025, 7(8): 1346-1354.

Liu Z, Vinskus J, et al. Fast and accurate ring strain energy predictions with machine learning and application in strain-promoted reactions [J]. JACS Au, 2025, 5(10): 4750-4761.

Pengmei Z, Lorpaiboon C, Guo S C, et al. geom2vec: Pretrained GNNs as geometric featurizers for conformational dynamics [EB/OL]. (2024-09-30) [2026-05-20]. https://arxiv.org/abs/2307.09484.

Appavoo S D, Huh S, Diaz D B, et al. Conformational control of macrocycles by remote structural modification [J]. Chem Rev, 2019, 119(17): 9724-9752.

Boiko D A, Reschützegger T, Sanchez-Lengeling B, et al. Advancing molecular machine learning representations with stereoelectronics-infused molecular graphs [J]. Nat Mach Intell, 2025, 7(5): 771-781.

Efron B. Bootstrap methods: Another look at the jackknife [J]. Ann Stat, 1979, 7(1): 1-26.

白雪, 谭天阳. 雷公藤生殖及肝肾毒理机制研究进展[J]. 中国药物警戒, 2024, 21(10): 1192-1199.
熊贤招, 祝瑶露, 汪新娌, 等. 中药质量分析方法研究进展[J]. 中国医药科学, 2025, 15(1): 35-38.
Karschnia P, Nelson T A, Dietrich J. Mechanisms and treatment of cancer therapy-induced peripheral and central neurotoxicity [J]. Nat Rev Cancer, 2025, 25(12): 887-909.
赵宁, 韩著, 简颖琳, 等. 中药多糖结构表征及质量评价研究进展[J]. 中草药, 2024, 55(21): 7491-7506.
赵崇军, 贾哲, 汤阳, 等. 基于中药“毒-效”关系探讨中药毒性特征[J]. 环球中医药, 2024, 17(8): 1602-1606.
Zhou T T. Unveiling secrets of traditional Chinese medicine: Cutting-edge techniques in component analysis [J]. Chin Herb Med, 2025, 17(3): 484-487.
Li X Y, et al. Integrated toxicology of Aconitum carmichaelii Debx.: Bridging traditional toxicity-efficacy understanding and modern toxicology [J]. Front Pharmacol, 2025, 16: 1667059.
唐玺, 刘韶, 何鸽飞. 基于声表面波气相色谱技术和顶空-固相微萃取-气相-色谱质谱联用对不同产地苍术的鉴别和挥发油成分分析[J]. 中南药学, 2026, 24(1): 41-47.
葛薇. 超高效液相色谱-串联质谱法测定中药饮片中黄色素类染色剂的研究[J]. 实验室检测, 2025, 3(18): 44-46.
王帅. 基于液相色谱技术的中药制剂质量控制标准研究[J]. 品牌与标准化, 2025(5): 133-135.
巩亦萍, 顾志荣, 郭引兄, 等. 中药药效物质基础研究方法与思路的进展与展望[J]. 中成药, 2025, 47(4): 1211-1216.
Votkina D, Petunin P, Miliutina E, et al. Uncovering the role of chemical and electronic structures in plasmonic catalysis: The case of homolysis of alkoxyamines [J]. ACS Catal, 2023, 13(5): 2822-2833.
Burés J, Armstrong A, Blackmond D G. Explaining anomalies in enamine catalysis: “Downstream species” as a new paradigm for stereocontrol [J]. Acc Chem Res, 2016, 49(2): 214-222.
Richter W E. The increased Diels-Alder reactivity of umpolung tropone: Analysis of individual atoms and bonds using QTAIM and IQA along complete IRC paths [J]. J Phys Org Chem, 2024, 37(7): e4572.
Hui B Y K, Le Osmund Chin K, Lim J J L, et al. Strategies to enhance the electrochromic properties of conjugated polymers bearing pyromellitic diimide acceptors [J]. Chem, 2024, 19(12): e202400236.
Ree N, Wollschläger J M, Göller A H, et al. Atom-based machine learning for estimating nucleophilicity and electrophilicity with applications to retrosynthesis and chemical stability [J]. Chem Sci, 2025, 16(13): 5676-5687.
Ghidini A, Serra E, et al. On free energy calculations in drug discovery [J]. Acc Chem Res, 2025, 58(20): 3137-3145.
Kim G, Grams R J, et al. Advancing covalent ligand and drug discovery beyond cysteine [J]. Chem Rev, 2025, 125(14): 6653-6684.
Gaudry A, Pagni M, Mehl F, et al. A sample-centric and knowledge-driven computational framework for natural products drug discovery [J]. ACS Cent Sci, 2024, 10(3): 494-510.
Adediwura V A, Koirala K, Do H N, et al. Understanding the impact of binding free energy and kinetics calculations in modern drug discovery [J]. Expert Opin Drug Discov, 2024, 19(6): 671-682.
Qian R T, Xue J, Xu Y, et al. Alchemical transformations and beyond: Recent advances and real-world applications of free energy calculations in drug discovery [J]. J Chem Inf Model, 2024, 64(19): 7214-7237.
Boulougouri M, Vandergheynst P, Probst D. Molecular set representation learning [J]. Nat Mach Intell, 2024, 6(7): 754-763.
Corso G, Stark H, Jegelka S, et al. Graph neural networks [J]. Nat Rev Meth Primers, 2024, 4: 17.
Xu K, Hu W, Leskovec J, et al. How powerful are graph neural networks? [A] // 7th International Conference on Learning Representations [C]. New Orleans: ICLR, 2019: 1-17.
Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks [A] // Proceedings of the 5th International Conference on Learning Representations [C]. Toulon: ICLR, 2017:1-14.
Fang X M, Liu L H, Lei J Q, et al. Geometry-enhanced molecular representation learning for property prediction [J]. Nat Mach Intell, 2022, 4(2):127-134.
Li Y S, Wang W, Liu J, et al. Pre-training molecular representation model with spatial geometry for property prediction [J]. Comput Biol Chem, 2024, 109: 108023.
Wu Z Q, Ramsundar B, Feinberg E N, et al. MoleculeNet: A benchmark for molecular machine learning [J]. Chem Sci, 2017, 9:513-530.
Chupakhin E, Babich O, Prosekov A, et al. Spirocyclic motifs in natural products [J]. Molecules, 2019, 24(22): 4165.
Nakase I, Lai H, Singh N P, et al. Anticancer properties of artemisinin derivatives and their targeted delivery by transferrin conjugation [J]. Int J Pharm, 2008, 354(1/2): 28-33.
Crull E B, Jain A N, Hawkins P C D, et al. Unmasking the true identity of rapamycin’s minor conformer [J]. J Nat Prod, 2023, 86(7):1862-1869.
Zhu J H, Wu K, Wang B H, et al. 𝒪-GNN:Incorporating ring priors into molecular modeling [A] // Proceedings of the 11th International Conference on Learning Representations [C]. Kigali: ICLR, 2023: 1-19:
Qiao J B, Jin J R, Wang D, et al. A self-conformation-aware pre-training framework for molecular property prediction with substructure interpretability [J]. Nat Commun, 2025, 16: 4382.
Luo Y Z, Yang K, Hong M, et al. MolFM: A multimodal molecular foundation model [EB/OL]. (2023-06-06) [2026-05-20]. https://arxiv.org/abs/2307.09484.
Axelrod S, Gómez-Bombarelli R.Molecular machine learning with conformer ensembles [J]. Mach Learn Sci Technol, 2023, doi:10.1088/2632-2153/acefa7.
Ru J L, Li P, Wang J N, et al. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines [J]. J Cheminf, 2014, 6(1): 13.
Gao K, et al. HERB 2.0: An updated database integrating clinical and experimental evidence for traditional Chinese medicine [J]. Nucleic Acids Res, 2025, 53(D1): D1404-D1414.
Ramsundar B, Eastman P, Walters P, et al. Deep learning for the life sciences: Applying deep learning to genomics, microscopy, drug discovery, and more [M]. Sebastopol: O'Reilly Media, 2019: 39-58.
Weininger D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules [J]. J Chem Inf Comput Sci, 1988, 28: 31-36.
Zhu Z, Li L, Wang Z, et al. PGL: A graph learning library for large-scale heterogeneous graphs [A] // Proceedings of the 30th ACM International Conference on Information & Knowledge Management [C]. New York: ACM, 2021: 4884-4888.
Ba J L, Kiros J R, Hinton G E. Layer normalization [EB/OL]. (2016-07-21) [2026-02-10]. https://arxiv.org/ abs/1607.06450.
Chen Y H, Tang X, Qi X B, et al. Learning graph normalization for graph neural networks [EB/OL]. (2020-09-24) [2026-02-10]. https://arxiv.org/abs/2009.11746.
Kingma D P, Ba J. Adam: A method for stochastic optimization [EB/OL]. (2014-12-22) [2026-02-10]. https://arxiv.org/abs/1412.6980.
Croitoru F A, Ristea N C, Ionescu R T, et al. Learning rate curriculum [J]. Int J Comput Vis, 2025, 133(1): 291-314.
Li L L, Zhang Y P, Wang G H, et al. Kolmogorov-Arnold graph neural networks for molecular property prediction [J]. Nat Mach Intell, 2025, 7(8): 1346-1354.
Liu Z, Vinskus J, et al. Fast and accurate ring strain energy predictions with machine learning and application in strain-promoted reactions [J]. JACS Au, 2025, 5(10): 4750-4761.
Pengmei Z, Lorpaiboon C, Guo S C, et al. geom2vec: Pretrained GNNs as geometric featurizers for conformational dynamics [EB/OL]. (2024-09-30) [2026-05-20]. https://arxiv.org/abs/2307.09484.
Appavoo S D, Huh S, Diaz D B, et al. Conformational control of macrocycles by remote structural modification [J]. Chem Rev, 2019, 119(17): 9724-9752.
Boiko D A, Reschützegger T, Sanchez-Lengeling B, et al. Advancing molecular machine learning representations with stereoelectronics-infused molecular graphs [J]. Nat Mach Intell, 2025, 7(5): 771-781.
Efron B. Bootstrap methods: Another look at the jackknife [J]. Ann Stat, 1979, 7(1): 1-26.

2026年第57卷第16期

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文章信息

doi: 10.7501/j.issn.0253-2670.2026.16.005

接收时间：2026-04-06
首发时间：2026-10-22
出版时间：2026-08-28

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收稿日期：2026-04-06

基金

国家自然科学基金面上项目

重庆市璧山区中医药领航联合专项重大项目

重庆市自然科学基金创新发展联合基金

作者信息

^1.重庆科技大学化学化工学院, 重庆 401331

^2.重庆中医药学院中药学院, 制药过程数字化重庆市重点实验室, 重庆 402760

^3.重庆科技大学化学化工学院, 重庆 401331

^4.重庆中医药学院中药学院, 制药过程数字化重庆市重点实验室, 重庆 402760

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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