Multidimensional breakthroughs in innovative drug development: from molecular design to clinical practice

doi:10.3969/j.issn.1006-7795.2026.03.001

Abstract

Abstract: Conventional drug discovery and development is constrained by structural bottlenecks, including protracted timelines, high costs, low clinical success rates, and the rapid emergence of resistance associated with single-target strategies. With accelerating interdisciplinary convergence and continuous technological innovation, innovative drug R&D is undergoing a comprehensive, end-to-end transformation spanning target discovery, drug design, and clinical translation. This review systematically summarizes three major frontiers: (i) multi-omics integration and artificial intelligence (AI)-enabled precision target identification; (ii) intelligent drug design coupled with innovations in advanced drug delivery and controlled-release systems; and (iii) precision-medicine-driven individualized therapy. By integrating advances from nanotechnology, materials science, synthetic biology, and related disciplines, we elucidate a closed-loop pathway that translates molecular mechanisms into actionable clinical value, thereby further catalyzing drug innovation and supporting transformative progress in biomedicine. In alignment with national strategic priorities for the biopharmaceutical sector, we propose a new R&D paradigm centered on clinical value, supported by technological innovation, and advanced through multidisciplinary integration, providing both theoretical insights and practical reference for achieving high-level scientific and technological self-reliance and strengthening the foundation of biopharmaceutical industry development.

Key words: innovative drug development, multi-omics technologies, artificial intelligence, precision medicine, individualized therapy, advanced drug delivery systems

CLC Number:

R9
TP18

Wang Yaoqi, Huo Ran, Zhang Shuang, Sun Qi, Zhang Xiaoyi, Cui Chunying, Zhang Qiang. Multidimensional breakthroughs in innovative drug development: from molecular design to clinical practice[J]. Journal of Capital Medical University, doi: 10.3969/j.issn.1006-7795.2026.03.001.

References

［1］Dowden H, Munro J. Trends in clinical success rates and therapeutic focus［J］. Nat Rev Drug Discov, 2019, 18(7): 495-496.
［2］林敏聪, 张胜军. 转化医学——搭建药物研发临床前到临床研究的桥梁［J］. 临床合理用药杂志, 2019, 12(22): 180-181.
［3］Ma M X, Huang M, He Y T, et al. Network medicine: a potential approach for virtual drug screening［J］. Pharmaceuticals, 2024, 17(7): 899.
［4］Li X, Liu Z Q, Liao J, et al. Network pharmacology approaches for research of traditional Chinese medicines［J］. Chin J Nat Med, 2023, 21(5): 323-332.
［5］Chen H, Shi Y, Yuan M L, et al. Integrating multi-omics and artificial intelligence fuels advanced target identification and drug discovery［J］. Biotechnol Adv, 2026, 87: 108785.
［6］Wei S M, Tang W C, Chen D, et al. Multiomics insights into the female reproductive aging［J］. Ageing Res Rev, 2024, 95: 102245.
［7］Akhoundova D, Rubin M A. Clinical application of advanced multi-omics tumor profiling: shaping precision oncology of the future［J］. Cancer Cell, 2022, 40(9): 920-938.
［8］Chen P, Zhao P, Hu M, et al. HnRNP A2/B1 as a potential anti-tumor target for triptolide based on a simplified thermal proteome profiling method using XGBoost［J］. Phytomedicine, 2023, 117: 154929.
［9］Pun F W, Ozerov I V, Zhavoronkov A. AI-powered therapeutic target discovery［J］. Trends Pharmacol Sci, 2023, 44(9): 561-572.
［10］Zhang K, Yang X, Wang Y F, et al. Artificial intelligence in drug development［J］. Nat Med, 2025, 31(1): 45-59.
［11］吴梅花, 陈小娥, 林琳, 等. 人工智能技术在药物设计领域的应用与展望［J］. 中国处方药, 2025, 23(24): 120-123.
［12］Choi Y, Kim S, Kim Y, et al. Abstract 4719: Discovery of potential synergistic targets for PKMYT1 inhibitor through AI-assisted drug combination screening in an MPS-based vascularized metastatic gastric cancer model［J］. Cancer Res, 2025, 85(8_Supplement_1): 4719.
［13］Li J Y, Zhang J M, Guo R, et al. Progress of machine learning in the application of small molecule druggability prediction［J］. Eur J Med Chem, 2025, 285: 117269.
［14］Tong J B, Zhang X, Bian S, et al. Drug design, molecular docking, and admet prediction of CCR5 inhibitors based on QSAR study^①［J］. Chin J Struct Chem, 2022, 41(2): 2202001-2202013.
［15］Medhi B, Prajapat M, Insilico-Study. Molecular docking study and ADMET prediction of phytoconstituents of Withania Somniferra Flem. in Alzheimer’s disease: an insilico explorative study［J］. Alzheimer’s & Dement, 2022, 18(S10): e065519.
［16］Zheng S J, Tan Y H, Wang Z Y, et al. Accelerated rational PROTAC design via deep learning and molecular simulations［J］. Nat Mach Intell, 2022, 4(9): 739-748.
［17］Al Ragib A, Chakma R, Dewan K, et al. Current advanced drug delivery systems: challenges and potentialities［J］. J Drug Deliv Sci Technol, 2022, 76: 103727.
［18］刘晶晶, 殷果, 黄小玲, 等. 纳米药物研究现状［J］. 中国药业, 2025, 34(2): 1-6.
［19］Wang B L, Shen B, Xiang W Q, et al. Advances in the study of LNPs for mRNA delivery and clinical applications［J］. Virus Genes, 2024, 60(6): 577-591.
［20］Vernieri C, Milano M, Mennitto A, et al. Safety and antitumor activity of weekly carboplatin plus paclitaxel in metastatic breast cancer: a ten years, single-institution, retrospective analysis［J］. J Clin Oncol, 2017, 35(suppl 15): e12556.
［21］Dai Z D, Wu Z S, Li W J, et al. Real-time near-infrared II fluorescence navigation of magnetic nanorobots for image-guided therapy［J］. Sci Adv, 2026, 12(1): eaea5126.
［22］Li M H, Lin C, Shen A J, et al. A smart responsive nanotheranostic system for MRI of tumor response to immunotherapy and enhanced synergism of thermo-immunotherapy［J］. Adv Funct Mater, 2024, 34(23): 2315171.
［23］李翀, 吴俊伟. 智能响应药物递送技术的开发与前沿进展［J］. 药学进展, 2021, 45(5): 321-324.
［24］Cheng H X, Liao J, Ma Y H, et al. Advances in targeted therapy for tumor with nanocarriers: a review［J］. Mater Today Bio, 2025, 31: 101583.
［25］Shi S, Zhou M, Li X, et al. Synergistic active targeting of dually integrin α v β 3/CD44-targeted nanoparticles to B16F10 tumors located at different sites of mouse bodies［J］. J Controlled Release, 2016, 235: 1-13.
［26］Mashele S S. Stimuli-responsive, cell-mediated drug delivery systems: engineering smart cellular vehicles for precision therapeutics［J］. Pharmaceutics, 2025, 17(8): 1082.
［27］Lee J, Lee J H, Chakraborty K, et al. Exosome-based drug delivery systems and their therapeutic applications［J］. RSC Adv, 2022, 12(29): 18475-18492.
［28］Pearson R M, Nurunnabi M. Forum on biomaterials for immune and gene delivery［J］. ACS Appl Bio Mater, 2024, 7(8): 4816-4817.
［29］Saunders K H, Igel L I, Aronne L J. An update on naltrexone/bupropion extended-release in the treatment of obesity［J］. Expert Opin Pharmacother, 2016, 17(16): 2235-2242.
［30］Quesada O, Henry T D. New generation drug eluting stents: closing the gap in sex disparity in cardiovascular outcomes in women［J］. Catheter Cardiovasc Interv, 2021, 97(5): 805-806.
［31］Lori Zoudani E, Nguyen N T, Kashaninejad N. Microneedle optimization: toward enhancing microneedles functionality and breaking the traditions［J］. Small Struct, 2024, 5(12): 2400121.
［32］Schneider D, Bianchini G, Horgan D, et al. Establishing the evidence bar for molecular diagnostics in personalised cancer care［J］. Public Health Genomics, 2015, 18(6): 349-358.
［33］Normanno N, Apostolidis K, De Lorenzo F, et al. Cancer biomarkers in the era of precision oncology: addressing the needs of patients and health systems［J］. Semin Cancer Biol, 2022, 84: 293-301.
［34］Hicks J K, Howard R, Reisman P, et al. Integrating somatic and germline next-generation sequencing into routine clinical oncology practice［J］. JCO Precis Oncol, 2021, 5: 884-895.
［35］初晓阳, 高春红, 葛成, 等. 以患者为中心的个体化制剂新技术研究进展［J］. 中国新药杂志, 2018, 27(4): 409-416.
［36］Tracy T, Wu L, Liu X, et al. 3D printing: innovative solutions for patients and pharmaceutical industry［J］. Int J Pharm, 2023, 631: 122480.
［37］Tsai A Y, Greene A C. 3D printing in pediatric surgery［J］. Semin Pediatr Surg, 2024, 33(1): 151385.［38］Feng W Q, Zhao Y F, Yu D R, et al. Advances in tumor antigen vaccines: a new frontier in cancer immunotherapy［J］. Int J Med Sci, 2025, 22(16): 4406-4417.
［39］Arabi F, Torabi-Rahvar M, Shariati A, et al. Antigenic targets of CAR T cell therapy. A retrospective view on clinical trials［J］. Exp Cell Res, 2018, 369(1): 1-10.
［40］Gao B, Yeap S, Clements A, et al. Evidence for therapeutic drug monitoring of targeted anticancer therapies［J］. J Clin Oncol, 2012, 30(32): 4017-4025.
［41］Pollard T D, Ong J J, Goyanes A, et al. Electrochemical biosensors: a nexus for precision medicine［J］. Drug Discov Today, 2021, 26(1): 69-79.
［42］Avram S, Halip L, Curpan R, et al. Novel drug targets in 2024［J］. Nat Rev Drug Discov, 2025, 24(5): 323-323.

[1]	Lou Xiaoting, Rong Lulu, Wang Yuanhang, Wang Qing, Xu Mengke, Zhang Liang, Zhang Yungang. Research on establishing an artificial intelligence model to assist in the counting of eosinophils in nasal polyps [J]. Journal of Capital Medical University, 2026, 47(3): 490-496.
[2]	Liang Zheng, Liu Kuiliang, Ning Tingting, Li Peng, Zhang Shutian#, Wei Yongqiu. The application of artificial intelligence in the diagnosis and treatment of cholangiocarcinoma [J]. Journal of Capital Medical University, 2026, 47(1): 190-197.
[3]	Liu Honglei, Yang Yingliang, Li Ronghao, Zhu Congmin, Zhang Xu. Application of artificial intelligence in the study of cancer diagnosis and treatment research [J]. Journal of Capital Medical University, 2025, 46(3): 395-400.
[4]	Luo Yi, Qin Qi, Wang Peihao, Guo Kangshun, Zhang Xinping, Cui Yong. Application and research progress of artificial intelligence combined with spectral diagnosis technology in the identification of lung cancer tissues [J]. Journal of Capital Medical University, 2023, 44(6): 1095-1102.