欢迎来到OAJRC平台！

期刊栏目

投稿

审稿

期刊目次

加入编委

期刊订阅

添加您的邮件地址以接收即将发行期刊数据：

当前位置：国际教育学 > 2025年 7卷 (12)期

Open Access Article

International Journal of Education. 2025; 7: (12) ; 26-35 ; DOI: 10.12208/j.ije.20250423.

Curriculum redesign of drug design education based on frontier real-world case studies
基于前沿真实案例的药物设计课程重构

作者： 马雪兰^1,2#, 刘威^2#, 王贯^3#, 胡滨¹, 李宁⁴ *, 刘博^1,2 *

1 大连医科大学附属第二医院肿瘤精准药物研究中心辽宁大连
2 四川大学生物治疗全国重点实验室四川成都

3 四川大学华西医院华西护理创新研究中心四川成都

4 沈阳药科大学中药学院辽宁沈阳

*通讯作者：李宁,单位：沈阳药科大学中药学院辽宁沈阳；刘博,单位：大连医科大学附属第二医院肿瘤精准药物研究中心辽宁大连四川大学生物治疗全国重点实验室四川成都；

大连医科大学教学改革项目（项目编号：DYLX25031）大连医科大学附属第二医院教学项目（项目编号：2024CXCYPY01）

发布时间: 2025-12-12 总浏览量: 15

PDF 全文下载引用本文

摘要

在新医科建设与人工智能技术迅速发展的背景下，药物设计课程面临更新滞后、科研脱节、学生创新力不足等问题。本文以“AI驱动的靶标-先导教学闭环”为核心，从生成式、结构驱动与表型驱动三条主线出发，重构药物设计课程体系。通过引入AlphaFold结构预测、生成式分子设计、AI药效评估等真实科研案例，构建“教学-科研-创新”一体化课程模式。基于课程项目评分量表与匿名问卷反馈的试点教学证据显示，该模式显著提升了学生AI工具使用熟练度与跨学科协作能力，且多数学生认为真实案例与项目制训练显著增强了其科研思维与循证意识。

关键词： 药物设计课程；人工智能；教学改革；案例教学；课程思政

Abstract

Against the backdrop of the “New Medical Sciences” initiative and the rapid development of artificial intelligence technologies, drug design courses are facing problems such as outdated content, disconnection from frontier research, and insufficient student innovation capability. Centered on an “AI-driven target-to-lead teaching loop”, this paper reconstructs the curriculum system of drug design along three main routes: generative design, structure-based design, and phenotype-based design. By introducing authentic research cases such as AlphaFold-based structure prediction, generative molecular design, and AI-powered efficacy evaluation, we build an integrated "teaching–research–innovation" course model. Based on rubric-based course project assessment and anonymous survey feedback from the pilot implementation, this model substantially improved students' proficiency with AI tools and interdisciplinary collaboration, and most students reported that real-world cases and project-based training strengthened their research thinking and evidence-based awareness.

Key words： Drug design course; Artificial intelligence; Teaching reform; Case-based teaching; Curriculum-based ethics education

参考文献 References

[1] 国务院办公厅. 关于加快医学教育创新发展的指导意见（国办发〔2020〕34号）[EB/OL]. (2020-09-17)[2025-11-10]. 可获得:

https://www.gov.cn/zhengce/content/2020-09/23/content_5546373.htm.

[2] 中共中央, 国务院. 教育强国建设规划纲要（2024—2035年）[EB/OL]. (2025-05-20)[2025-11-10]. 可获得:

https://www.gov.cn/zhengce/202501/content_6999914.htm.

[3] 教育部. 国家教育数字化战略行动2025年部署会召开[EB/OL]. (2025-03-28)[2025-11-10]. 可获得:

http://www.moe.gov.cn/jyb_xwfb/gzdt_gzdt/moe_1485/202503/t20250328_1185222.html.

[4] 郑永和, 王佳宁, 陶丹. 科教协同促进科学教育高质量发展: 内涵、意义、现状与路径[J]. 电化教育研究, 2024, 45(10): 5-11.

[5] FERREIRA FJN, CARNEIRO AS. AI-driven drug discovery: A comprehensive review[J]. ACS Omega, 2025, 10(23): 23889-23903.

[6] TONG X, QU N, KONG X, et al. Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery[J]. Nat Commun, 2024, 15(1): 5378.

[7] REN F, ALIPER A, CHEN J, et al. A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models[J]. Nat Biotechnol, 2025, 43(1): 63-75.

[8] DESAI D, KANTLIWALA SV, VYBHAVI J, et al. Review of AlphaFold 3: Transformative advances in drug design and therapeutics[J]. Cureus, 2024, 16(7): e63646.

[9] ABRAMSON J, ADLER J, DUNGER J, et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3[J]. Nature, 2024, 630(8016): 493-500.

[10] 澳门理工大学应用科学学院. 人工智能药物发现博士学位课程[EB/OL]. [2025-11-10]. 可获得:

https://www.mpu.edu.mo/esca/zh/phd_aidd.php.

[11] 杨龙华, 单丽红, 张恩, 等. 人工智能在计算机辅助药物设计模块教学中的应用与思考[J]. 药学教育, 2025, 41(5): 27-32.

[12] 杜佳恬, 李琰, 何勤, 等. 在协调发展理念指导下的药学课程结构改革思考与初步实践[J]. 四川大学学报(医学版), 2022, 53(2): 281-284.

[13] ANGELO JS, GUEDES IA, BARBOSA HJC, et al. Multi- and many-objective optimization: Present and future in de novo drug design[J]. Front Chem, 2023, 11: 1288626.

[14] ZENG X, WANG F, LUO Y, et al. Deep generative molecular design reshapes drug discovery[J]. Cell Rep Med, 2022, 3(12): 100794.

[15] ABEER ANMN, URBAN NM, WEIL MR, et al. Multi-objective latent space optimization of generative molecular design models[J]. Patterns (N Y), 2024, 5(10): 101042.

[16] YANG Y, CHEN G, LI J, et al. Enabling target-aware molecule generation to follow multi objectives with Pareto MCTS[J]. Commun Biol, 2024, 7(1): 1074.

[17] KADAN A, RYCZKO K, LLOYD E, et al. Guided multi-objective generative AI to enhance structure-based drug design[J]. Chem Sci, 2025, 16(29): 13196-13210.

[18] LOEFFLER HH, HE J, TIBO A, et al. Reinvent 4: Modern AI-driven generative molecule design[J]. J Cheminform, 2024, 16(1): 20.

[19] VARADI M, BERTONI D, MAGANA P, et al. AlphaFold Protein Structure Database in 2024: Providing structure coverage for over 214 million protein sequences[J]. Nucleic Acids Res, 2024, 52(D1): D368-D375.

[20] BLANCO-GONZÁLEZ A, CABEZÓN A, SECO-GONZÁLEZ A, et al. The role of AI in drug discovery: Challenges, opportunities, and strategies[J]. Pharmaceu-ticals (Basel), 2023, 16(6): 891.

[21] MOFFAT JG, VINCENT F, LEE JA, et al. Opportunities and challenges in phenotypic drug discovery: An industry perspective[J]. Nat Rev Drug Discov, 2017, 16(8): 531-543.

[22] LIU G, CATACUTAN DB, RATHOD K, et al. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii[J]. Nat Chem Biol, 2023, 19(11): 1342-1350.

[23] STOKES JM, YANG K, SWANSON K, et al. A deep learning approach to antibiotic discovery[J]. Cell, 2020, 180(4): 688-702.e13.

[24] LIU X, LU Y, CHEN Q, et al. Deep learning-based discovery of tetrahydrocarbazoles as broad-spectrum antitumor agents and click-activated strategy for targeted cancer therapy[J]. Acta Pharm Sin B, 2025.

[25] BUTTENSCHOEN M, MORRIS GM, DEANE CM. PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences[J]. Chem Sci, 2024, 15(9): 3130-3139.

[26] VAN TILBORG D, ALENICHEVA A, GRISONI F. Exposing the limitations of molecular machine learning with activity cliffs[J]. J Chem Inf Model, 2022, 62(23): 5938-5951.

[27] WOGNUM C, ASH JR, ALDEGHI M, et al. A call for an industry-led initiative to critically assess machine learning for real-world drug discovery[J]. Nat Mach Intell, 2024, 6(10): 1120-1121.

[28] WORLD HEALTH ORGANIZATION. WHO releases AI ethics and governance guidance for large multi-modal models[EB/OL]. (2024-01-18)[2025-11-10]. Available from: https://www.who.int/news/item/18-01-2024-who-releases-ai-ethics-and-governance-guidance-for-large-multi-modal-models.

[29] VARADI M, ANYANGO S, DESHPANDE M, et al. AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-sequence space with high-accuracy models[J]. Nucleic Acids Res, 2022, 50(D1): D439-D444.

[30] CHENG J, NOVATI G, PAN J, et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense[J]. Science, 2023, 381(6664): eadg7492.

[31] ALM RA, LAHIRI SD. Narrow-spectrum antibacterial agents—benefits and challenges[J]. Antibiotics (Basel), 2020, 9(7): 418.

[32] KHURANA MP, CURRAN-SEBASTIAN J, BHATT S, et al. Modelling the implementation of narrow versus broader spectrum antibiotics in the empiric treatment of E. coli bacteraemia[J]. Sci Rep, 2024, 14(1): 16986.

[33] STRUBELL E, GANESH A, MCCALLUM A. Energy and policy considerations for deep learning in NLP[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy: Association for Computational Linguistics, 2019: 3645-3650.

引用本文

马雪兰#, 刘威#, 王贯#, 胡滨, 李宁, 刘博, 基于前沿真实案例的药物设计课程重构[J]. 国际教育学, 2025; 7: (12) : 26-35.

入驻平台

回到顶部

搜索文章: