收藏切换
Artificial intelligence for drug delivery: Yesterday, today and tomorrow
收藏切换
PDF
Acta Pharmaceutica Sinica B | 2026, 16(4) : 2068 - 2092
Less
收藏切换
Acta Pharmaceutica Sinica B | 2026, 16(4): 2068-2092
Review
Artificial intelligence for drug delivery: Yesterday, today and tomorrow
Full
Yiyang Wu1, Nannan Wang1, Ping Xiong1, Ruifeng Wang1,2, Jiayin Deng1, Defang Ouyang1,3
Affiliations
    1 State Key Laboratory of Mechanism and Quality of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China;
    2 College of Chemistry and Chemical Engineering, Henan University, Kaifeng 475004, China;
    3 Faculty of Health Sciences, University of Macau, Macau 999078, China
doi: 10.1016/j.apsb.2025.09.022
Outline
收藏切换
The global pharmaceutical drug delivery market is forecasted to grow to USD 2546.0 billion by 2029. The expanding pharmaceutical market urgently needs a more efficient drug research and development paradigm. Artificial intelligence (AI) is revolutionizing drug delivery by offering alternatives to traditional trial-and-error experimental approaches. This review systematically traces the technological evolution from early simple models to current advanced AI algorithms in various applications, ranging from formulation optimization to the prediction of critical formulation parameters and de novo material design. To enhance the reliability of AI applications in drug delivery, we present comprehensive guidelines and “Rule of Five” (Ro5) principles to systematically direct researchers in utilizing AI in formulation development. This “Ro5” includes the following criteria: a formulation dataset containing at least 500 entries, coverage of a minimum of 10 drugs and all significant excipients, appropriate molecular representations for both drugs and excipients, inclusion of all critical process parameters, and utilization of suitable algorithms and model interpretability. The review concludes with insights into emerging trends and future directions, including the utilization of large language models, multidisciplinary collaboration opportunities, talent development, and culture transformation, aimed at facilitating a paradigm shift toward AI-driven drug formulation development.
Drug delivery  /  Rational formulation design  /  Artificial intelligence  /  Machine learning  /  Deep learning  /  Formulation prediction  /  Rule of five  /  Multidisciplinary integration
Yiyang Wu, Nannan Wang, Ping Xiong, Ruifeng Wang, Jiayin Deng, Defang Ouyang. Artificial intelligence for drug delivery: Yesterday, today and tomorrow[J]. Acta Pharmaceutica Sinica B, 2026 , 16 (4) : 2068 -2092 . DOI: 10.1016/j.apsb.2025.09.022
Year 2026 volume 16 Issue 4
PDF
8
5
Cite this Article
BibTeX
Article Info
doi: 10.1016/j.apsb.2025.09.022
  • Receive Date:2025-03-21
  • Online Date:2026-09-17
Article Data
Affiliations
History
  • Received:2025-03-21
  • Revised:2025-08-22
Affiliations
References
Share
https://castjournals.cast.org.cn/joweb/apsb/EN/10.1016/j.apsb.2025.09.022
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT