创新型跨学科融合导向——人工智能时代下药物分析教学模式探索

Innovative interdisciplinary integration oriented: exploration of the teaching model for Pharmaceutical Analysis in the era of artificial intelligence

摘要

在人工智能和大数据时代，药物分析教学迎来了创新发展的契机。药物分析作为药学教育的重要核心课程，不仅涉及药物研发和质量控制，还涵盖药物在人体内的代谢与分布。为了适应时代发展，教学模式需融合传统分析技术与计算药剂学等新兴科技，如分子动力学模拟和机器学习。这些技术的引入可以帮助学生在实践中提升分析与创新能力。此外，思政教育也被纳入教学中，以培养学生的社会责任感和职业道德。通过案例教学和职业情境模拟，学生能够树立科研诚信和高度责任感，从而更好地应对未来职业挑战。未来，药物分析教学将继续深化跨学科融合，注重将前沿技术与行业需求结合，培养具备创新能力与实践技能的复合型人才。

Abstract

In the era of artificial intelligence and big data, pharmaceutical analysis education is undergoing innovative development. As a core course in pharmaceutical education, pharmaceutical analysis includes drug development, quality control, and the study of drug metabolism and distribution in the human body. To adapt to technological advancements, teaching methods now integrate traditional analytical techniques with emerging technologies such as computational pharmaceutics, molecular dynamics simulations, and machine learning. These innovations help students enhance their analytical and creative skills through practical application. Additionally, ideological and political education has been incorporated to foster a sense of social responsibility and professional ethics in students. By using case studies and professional scenario simulations, students develop a strong sense of research integrity and responsibility, preparing them for future challenges in their careers. Looking forward, pharmaceutical analysis education will continue to deepen interdisciplinary integration, emphasizing the combination of cutting-edge technologies with industry needs, and training well-rounded professionals with both innovative thinking and practical skills.

关键词

药物分析 / 人工智能 / 跨学科 / 思政教育

Key words

pharmaceutical analysis / artificial intelligence / interdisciplinary / ideological and political education

参考文献

[1] 本刊评论员. 强化高等教育龙头作用做有理想、负责任的行动主义者[J]. 中国高等教育, 2024(2): 1.

[2] 许华容, 李清, 尹艺迪, 等. 药学专业药物分析实验在线虚拟仿真教学的探索与创新[J]. 高等药学教育研究, 2020(2): 22-26.

[3] WANG W, YE Z, GAO H, et al. Computational pharmaceutics - A new paradigm of drug delivery[J]. Journal of Controlled Release, 2021, 338: 119-136.

[4] GAO H, JIA H, DONG J, et al. Integrated in silico formulation design of self-emulsifying drug delivery systems[J]. Acta Pharmaceutica Sinica B, 2021, 11(11): 3585-3594.

[5] FENG Y, ZHANG S. Prediction of drug-drug interaction using an attention-based graph neural network on drug molecular graphs[J]. Molecules, 2022, 27(9): 3004.

[6] HEIKAMP K, BAJORATH J. Support vector machines for drug discovery[J]. Expert Opinion on Drug Discovery, 2014, 9(1): 93-104.

[7] SHI H, LIU S, CHEN J, et al. Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure[J]. Genomics, 2019, 111(6): 1839-1852.

[8] JAMESON C J, WANG X, MURAD S. Molecular dynamics simulations of enantiomeric separations as an interfacial process in HPLC[J]. AIChE Journal, 2021, 67(3): e17143.

[9] RAFFERTY J L, SIEPMANN J I, SCHURE M R. Mobile phase effects in reversed-phase liquid chromatography: A comparison of acetonitrile/water and methanol/water solvents as studied by molecular simulation[J]. Journal of Chromatography A, 2011, 1218(16): 2203-2213.

[10] JIANG D, WU Z, HSIEH C, et al. Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models[J]. Journal of Cheminformatics, 2021, 13(1): 12.