Motif-Attention Enhanced Graph Convolutional Networks for Drug-Target Interaction Prediction: Improved Generalization Under Cold-Start Conditions

Muhammad Yusuf ML; Isman Kurniawan

doi:10.55681/armada.v4i9.3540

Authors

Muhammad Yusuf ML School of Computing Telkom University Bandung, Indonesia
Isman Kurniawan School of Computing Telkom University Bandung, Indonesia

DOI:

https://doi.org/10.55681/armada.v4i9.3540

Keywords:

Drug-Target Interaction, Graph Convolutional Network, Attention Mechanism, Motif Learning, Cold-Start Prediction, Davis Dataset

Abstract

Accurate prediction of drug-target interactions (DTI) remains challenging, especially in cold-start scenarios involving unseen drugs or proteins. This study proposes a motif-attention augmented Graph Convolutional Network (GCN) framework that integrates structural motif information through an attention mechanism to improve molecular and protein representation learning. The proposed model was evaluated on the Davis kinase inhibitor dataset using four settings: random split, cold-drug, cold-protein, and cold-both. Results show that motif-attention significantly improves generalization in difficult scenarios, achieving an 11.19% reduction in Mean Squared Error (MSE) and a 15.70% increase in Concordance Index (CI) under cold-both evaluation, along with an 11.04% MSE reduction under cold-drug evaluation. Pearson correlation also improved from 0.0694 to 0.2234 in cold-both and from 0.3606 to 0.4686 in cold-drug settings. However, improvements in random and cold-protein splits were limited. These findings indicate that motif-level attention enhances robustness when prior structural information is limited, providing a promising approach for realistic DTI prediction in computational drug discovery.

Downloads

Download data is not yet available.

References

Ashburn, T. T., & Thor, K. B. (2004). Drug repositioning: Identifying and developing new uses for existing drugs. Nature Reviews Drug Discovery, 3(8), 673–683. https://doi.org/10.1038/nrd1468

Davis, M. I., Hunt, J. P., Herrgard, S., Ciceri, P., Wodicka, L. M., Pallares, G., Hocker, M., Treiber, D. K., & Zarrinkar, P. P. (2011). Comprehensive analysis of kinase inhibitor selectivity. Nature Biotechnology, 29(11), 1046–1051. https://doi.org/10.1038/nbt.1990

Fang, K., Zhang, Y., Du, S., & He, J. (2023). ColdDTA: Utilizing data augmentation and attention-based feature fusion for drug-target binding affinity prediction. Computers in Biology and Medicine, 164, 107372. https://doi.org/10.1016/j.compbiomed.2023.107372

Huang, K., Xiao, C., Glass, L. M., & Sun, J. (2021). MolTrans: Molecular interaction transformer for drug-target interaction prediction. Bioinformatics, 37(6), 830–836. https://doi.org/10.1093/bioinformatics/btaa880

Khoerunnisa, G., & Kurniawan, I. (2025). Enhancing drug-target affinity prediction through pre-trained language model and gated multi-head attention. International Journal of Advances in Intelligent Informatics, 11(2), 157–174. https://doi.org/10.26555/ijain.v11i2.1910

Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. Proceedings of the International Conference on Learning Representations (ICLR).

Nguyen, T., Le, H., Quinn, T. P., Nguyen, T., Le, T. D., & Venkatesh, S. (2021). GraphDTA: Predicting drug-target binding affinity with graph neural networks. Bioinformatics, 37(8), 1140–1147. https://doi.org/10.1093/bioinformatics/btaa921

Nguyen, T. M., Nguyen, T., & Tran, T. (2022). Mitigating cold-start problems in drug-target affinity prediction with interaction knowledge transferring. Briefings in Bioinformatics, 23(4), bbac269. https://doi.org/10.1093/bib/bbac269

Öztürk, H., Özgür, A., & Ozkirimli, E. (2018). DeepDTA: Deep drug-target binding affinity prediction. Bioinformatics, 34(17), i821–i829. https://doi.org/10.1093/bioinformatics/bty593

Öztürk, H., Ozkirimli, E., & Özgür, A. (2019). WideDTA: Prediction of drug-target binding affinity. arXiv preprint arXiv:1902.04166. https://arxiv.org/abs/1902.04166

Pahikkala, T., Airola, A., Pietilä, S., Shakyawar, S., Szwajda, A., Tang, J., & Aittokallio, T. (2015). Toward more realistic drug-target interaction predictions. Briefings in Bioinformatics, 16(2), 325–337. https://doi.org/10.1093/bib/bbu010

Torng, W., & Altman, R. B. (2019). Graph convolutional neural networks for predicting drug-target interactions. Journal of Chemical Information and Modeling, 59(10), 4131–4149. https://doi.org/10.1021/acs.jcim.9b00628

Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2018). Graph attention networks. Proceedings of the International Conference on Learning Representations (ICLR).

Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4–24. https://doi.org/10.1109/TNNLS.2020.2978386

Yu, J., & Gao, J. (2022). Molecular representation learning via heterogeneous motif graph neural networks. Proceedings of the International Conference on Machine Learning (ICML).

Yusuf, M. R. Y., & Kurniawan, I. (2025). Improving drug-target affinity prediction using dynamic graph attention network with multi-scale features and attention mechanism. International Journal of Intelligent Engineering and Systems, 18(3), 541–555. https://doi.org/10.22266/ijies2025.0430.36

Zhang, S., Liu, Y., & Xie, L. (2020). Molecular mechanics-driven graph neural network with multiplex graph for molecular structures. arXiv preprint arXiv:2011.07457. https://arxiv.org/abs/2011.07457