Citation: Meiru Liu, Yifei Xie, Shuang Li, Shiying Yang, Dezhi Yang, Zhengzheng Zhou, Li Zhang, Yang Lu, Guanhua Du. Multi-modal feature fusion graph neural networks for intelligent classification of pharmaceutical solid forms: Cocrystals, salts, and physical mixtures[J]. Chinese Chemical Letters, ;2026, 37(11): 112565. doi: 10.1016/j.cclet.2026.112565 shu

Multi-modal feature fusion graph neural networks for intelligent classification of pharmaceutical solid forms: Cocrystals, salts, and physical mixtures

Meiru Liu ^{a, 1} ,
Yifei Xie ^{a, 1} ,
Shuang Li ^a ,
Shiying Yang ^a ,
Dezhi Yang ^{a, *,} ,
Zhengzheng Zhou ^{b, *,} ,
Li Zhang ^{a, *,} ,
Yang Lu ^a ,
Guanhua Du ^a

a.
Beijing Key Laboratory of Innovative Drug Discovery and Polymorphic Research for Cerebrovascular Diseases, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
b.
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

ydz@imm.ac.cn

zhouzz418@smu.edu.cn

zhangl@imm.ac.cn

Received Date: 31 July 2025
Revised Date: 16 January 2026
Accepted Date: 1 March 2026
Available Online: 2 March 2026

Figures(5)

Pharmaceutical solid form engineering through co-crystallization represents a transformative strategy for enhancing drug bioavailability and physicochemical properties, yet remains hindered by labor-intensive experimental screening and the notable absence of computational models capable of simultaneously classifying co-crystals, salts, and physical mixtures. Here, we present an intelligent multi-modal graph neural network (GNN) framework that synergistically integrates molecular graph representations, molecular fingerprints, and quantum chemical descriptors to enable automated three-class classification of pharmaceutical solid forms, addressing a critical gap in current computational pharmaceutical research. Our innovative feature fusion strategy combines topological molecular graphs with traditional molecular fingerprints and computationally-derived quantum chemical properties, creating a comprehensive multi-dimensional representation of pharmaceutical compounds. The developed integrated model achieves exceptional classification accuracy exceeding 98% across all evaluation phases, demonstrating the superior performance of combined structural, topological, and electronic feature representations in complex multi-component pharmaceutical systems. Rigorous validation using three independent active pharmaceutical ingredient datasets, theophylline, ligustrazine, and piperazine, yields outstanding success rates, confirming the model's remarkable generalizability and practical applicability across diverse chemical spaces. Most significantly, our artificial intelligence (AI)-driven multi-modal predictions successfully guided the experimental synthesis of two novel co-crystalline phases: Emodin-ligustrazine and emodin-metformin, validating the predictive power of integrated molecular feature representations in real-world pharmaceutical development.
- Pharmaceutical solid form,
- Graph neural networks,
- Multi-class classification,
- Quantum chemistry,
- Co-crystallization,
- Emodin
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