Citation: Chang Zhang, Li Zhang, Dezhi Yang, Henry H.Y. Tong, Yang Lu, Zhengzheng Zhou. From traditional screening to machine learning facilitated development of pharmaceutical cocrystals[J]. Chinese Chemical Letters, ;2026, 37(11): 111828. doi: 10.1016/j.cclet.2025.111828 shu

From traditional screening to machine learning facilitated development of pharmaceutical cocrystals

Chang Zhang ^a ,
Li Zhang ^{b, *,} ,
Dezhi Yang ^b ,
Henry H.Y. Tong ^c ,
Yang Lu ^b ,
Zhengzheng Zhou ^{a, *,}

a.
NMPA Key Laboratory for Safety Evaluation of Cosmetics, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Hygiene Inspection & Quarantine Science, School of Public Health, Southern Medical University, Guangzhou 510515, China
b.
Beijing City Key Laboratory of Polymorphic Drugs, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
c.
Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China

zhangl@imm.ac.cn

zhouzz418@smu.edu.cn

Received Date: 6 July 2025
Revised Date: 4 September 2025
Accepted Date: 11 September 2025
Available Online: 11 September 2025

Figures(17)

Pharmaceutical cocrystals, multi-component crystalline systems, enhance the solubility, stability, and bioavailability of active pharmaceutical ingredients (APIs) through tailored intermolecular interactions. Traditional screening methods, such as solvent evaporation and mechanochemical approaches, have long been used to identify and prepare cocrystals but are often time-consuming and resource-intensive. Machine learning (ML) has emerged as a transformative tool, leveraging molecular descriptors and large datasets to streamline cocrystal development. This review traces the evolution from conventional screening techniques to ML-facilitated approaches, systematically exploring ML applications in cocrystal discovery, preparation, and physicochemical property evaluation. It examines key methodologies, challenges like data limitations, and opportunities for integrating ML with experimental methods to advance cocrystal design.
- Cocrystals,
- Crystal engineering,
- Crystallization,
- Machine learning,
- Artificial intelligence
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