基于多模态人工智能数据融合的中药药动学研究进展

doi:10.11871/jfdc.issn.2096-742X.2025.02.015

数据与计算发展前沿 ›› 2025, Vol. 7 ›› Issue (2): 149-160.

CSTR: 32002.14.jfdc.CN10-1649/TP.2025.02.015

doi: 10.11871/jfdc.issn.2096-742X.2025.02.015

基于多模态人工智能数据融合的中药药动学研究进展

郭惠婕^1,^*(),周泳杰²,许建真²

1.中国药科大学信息化建设管理处，江苏南京 211198
2.中国药科大学理学院，江苏南京 211198

收稿日期:2024-08-16 出版日期:2025-04-20 发布日期:2025-04-23
通讯作者: 郭惠婕
作者简介:郭惠婕，中国药科大学信息化建设管理处，硕士，主要研究方向为医药大数据及天然药物药代动力学。
本文承担工作为文献的收集整理以及整体内容的撰写。
Guo Huijie, hoding a master’s degree, is currently working at the Information Construction and Management Office of China Pharmaceutical University. Her main research directions include pharmaceutical big data and pharmacokinetics of natural medicines.
In this paper, she is responsible for literature collection and organization, as well as thesis writing.
E-mail: guohuijie@cpu.edu.cn
基金资助:
中央高校基本科研业务费专项资金资助项目(2632024RWPY04)

A Survey of Pharmacokinetics of Traditional Chinese Medicine Based on Multimodal Artificial Intelligence Data Fusion

GUO Huijie^1,^*(),ZHOU Yongjie²,XU Jianzhen²

1. Information Construction Management Office, China Pharmaceutical University, Nanjing, Jiangsu 211198, China
2. College of Science, China Pharmaceutical University, Nanjing, Jiangsu 211198, China

Received:2024-08-16 Online:2025-04-20 Published:2025-04-23
Contact: GUO Huijie

摘要/Abstract

摘要：

【目的】中药因其多成分、多靶点的作用机制而呈现出复杂性。人工智能因其卓越的数据处理能力，在中药成分靶标发现和药效研究中展现出巨大潜力，因此备受学术界和产业界的关注。然而，传统的人工智能方法在处理多维度数据时面临整合难题，导致训练过程模态缺失，从而影响药物识别的准确性和效果。【方法】本文探讨了深度学习等人工智能技术在中药药代动力学中的应用，以及多模态数据融合方法的实践手段，并将详细阐述这些方法在药物-靶标、药物-药物以及蛋白-蛋白相互作用中的应用。【结果】本文为中药靶标预测、药物相互作用和定量分析等药动学研究提供了多角度的知识服务。【结论】多模态数据融合方法的应用有望提升中药研究的效率与准确性，推动中药现代化进程，促进其在全球范围内的广泛应用。

关键词: 多模态数据融合, 人工智能, 中药学, 药代动力学

Abstract:

[Objective] Traditional Chinese medicine is complex because of its multi-component and multi-target mechanisms of action. With its exceptional data processing capabilities, artificial intelligence has demonstrated significant potential in the discovery of targets and efficacy research concerning traditional Chinese medicine ingredients, thereby attracting considerable attention from both academia and industry. However, conventional artificial intelligence methods encounter challenges in integrating multi-dimensional data during the training process, which can lead to the omission of modalities and subsequently impact the accuracy and effectiveness of drug identification. [Methods] This article explores the application of artificial intelligence technologies, particularly deep learning, in the pharmacokinetics of traditional Chinese medicine. It also examines practical methods for multi-modal data fusion and elaborates on the application of these methods in drug-target interactions, drug-drug interactions, and protein-protein interactions. [Results] This article provides multi-angle knowledge services for pharmacokinetic research such as target prediction of traditional Chinese medicine, drug interaction, and quantitative analysis. [Conclusions] The application of multi-modal data fusion methods is expected to improve the efficiency and accuracy of traditional Chinese medicine research, promote the modernization of traditional Chinese medicine, and promote its widespread application around the world.

Key words: multimodal data fusion, artificial intelligence, traditional Chinese medicine, pharmacokinetics

郭惠婕,周泳杰,许建真. 基于多模态人工智能数据融合的中药药动学研究进展[J]. 数据与计算发展前沿, 2025, 7(2): 149-160.

GUO Huijie,ZHOU Yongjie,XU Jianzhen. A Survey of Pharmacokinetics of Traditional Chinese Medicine Based on Multimodal Artificial Intelligence Data Fusion[J]. Frontiers of Data and Computing, 2025, 7(2): 149-160, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2025.02.015.

图/表 2

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基于多模态人工智能数据融合的中药药动学研究进展

A Survey of Pharmacokinetics of Traditional Chinese Medicine Based on Multimodal Artificial Intelligence Data Fusion

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