https://doi.org/10.1140/epjqt/s40507-025-00419-1
Research
Hybrid quantum neural networks for efficient protein-ligand binding affinity prediction
1
Department of Information Convergence Engineering, Pusan National University, Busandaehak-ro 63beon-gil, Geumjeong-street, 46241, Busan, Republic of Korea
2
School of Computer Science and Engineering, Pusan National University, Busandaehak-ro 63beon-gil, Geumjeong-street, 46241, Busan, Republic of Korea
Received:
25
February
2025
Accepted:
13
September
2025
Published online:
22
October
2025
Protein-ligand binding affinity is critical in drug discovery, but experimentally determining it is time-consuming and expensive. Artificial intelligence (AI) has been used to predict binding affinity, significantly accelerating this process. However, the high-performance requirements and vast datasets involved in affinity prediction demand increasingly large AI models, requiring substantial computational resources and training time. Quantum machine learning has emerged as a promising solution to these challenges. In particular, hybrid quantum-classical models can reduce the number of parameters while maintaining or improving performance compared to classical counterparts. Despite these advantages, challenges persist: why hybrid quantum models achieve these benefits, whether quantum neural networks (QNNs) can replace classical neural networks, and whether such models are feasible on noisy intermediate-scale quantum (NISQ) devices. This study addresses these challenges by proposing a hybrid quantum neural network (HQNN) that empirically demonstrates the capability to approximate non-linear functions in the latent feature space derived from classical embedding. The primary goal of this study is to achieve a parameter-efficient model in binding affinity prediction while ensuring feasibility on NISQ devices. Numerical results indicate that HQNN achieves comparable or superior performance and parameter efficiency compared to classical neural networks, underscoring its potential as a viable replacement. This study highlights the potential of hybrid QML in computational drug discovery, offering insights into its applicability and advantages in addressing the computational challenges of protein-ligand binding affinity prediction.
Key words: Hybrid quantum neural network / Protein-ligand binding affinity / Quantum machine learning / Quantum neural network
© The Author(s) 2025
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