PAMol: Pocket-Aware Drug Design Method with Hypergraph Representation of Protein Pocket Structure and Feature Fusion

Lin, Xiaoli; Liao, Xiongwei; Pang, Jun; Li, Bo; Zhang, Xiaolong

doi:10.24963/ijcai.2025/839

PAMol: Pocket-Aware Drug Design Method with Hypergraph Representation of Protein Pocket Structure and Feature Fusion

Xiaoli Lin, Xiongwei Liao, Jun Pang, Bo Li, Xiaolong Zhang

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 7545-7553.

https://doi.org/10.24963/ijcai.2025/839

Keywords

Machine Learning: ML: Deep learning architectures
Machine Learning: ML: Representation learning
Machine Learning: ML: Multi-modal learning
Multidisciplinary Topics and Applications: MTA: Bioinformatics

Abstract

Efficient generation of targeted drug molecules is crucial in the field of drug discovery. Most existing methods neglect the high-order information in the structure of protein pockets, limiting the performance of generated drug molecules. This paper proposes a pocket-aware drug design framework, namely PAMol, constructing the hypergraph to represent the spatial structure of protein pockets, effectively capturing high-order relations and neighborhood information within the pocket structures. This framework also fuses different modal embeddings from proteins and molecules, to generate high-quality molecules. In addition, a conditional molecule generation module uses the high-order structural information in protein pockets as constraints to more accurately generate molecules for specific targets. The performance of PAMol has been assessed by analyzing generated molecules in terms of vina score, high affinity, QED, SA, LogP, Lipinski, diversity, and time. Experimental results demonstrate the potential of PAMol for targeted drug design. The source code is available at https://github.com/YICHUANSYQ/PAMol.git.

Xiaoli Lin, Xiongwei Liao, Jun Pang, Bo Li, and Xiaolong Zhang. 2025. PAMol: Pocket-Aware Drug Design Method with Hypergraph Representation of Protein Pocket Structure and Feature Fusion. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 7545–7553. https://doi.org/10.24963/ijcai.2025/839

@inproceedings{ijcai2025p839,
  title     = {PAMol: Pocket-Aware Drug Design Method with Hypergraph Representation of Protein Pocket Structure and Feature Fusion},
  author    = {Lin, Xiaoli and Liao, Xiongwei and Pang, Jun and Li, Bo and Zhang, Xiaolong},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {7545--7553},
  year      = {2025},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2025/839},
  url       = {https://doi.org/10.24963/ijcai.2025/839},
}

TY  - CPAPER
TI  - PAMol: Pocket-Aware Drug Design Method with Hypergraph Representation of Protein Pocket Structure and Feature Fusion
AU  - Lin, Xiaoli
AU  - Liao, Xiongwei
AU  - Pang, Jun
AU  - Li, Bo
AU  - Zhang, Xiaolong
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 7545
EP  - 7553
DO  - 10.24963/ijcai.2025/839
UR  - https://doi.org/10.24963/ijcai.2025/839
ER  -