Prediction of Protein‐Ligand Binding Affinity by a Hybrid Quantum‐Classical Deep Learning Algorithm

Abstract

Rapid and accurate prediction of protein‐ligand binding affinity plays a vital role in high‐throughput drug screening. With the development of deep learning, increasingly accurate prediction models have been established. Deep learning may have ushered in an era of quantization, but the practical use of this theory for protein‐ligand binding affinity is still infrequent. Here, the introduction of the quantum algorithm into classical deep learning is described, which enables it to reliably predict protein‐ligand binding affinity using simple sequence information. Based on different deep learning models, including graph neural networks (GNN) and convolutional neural networks (CNN), corresponding quantum hybrid deep learning models have been constructed and compared to the classical models. This study has shown that the quantum algorithm can achieve considerable accuracy and good generalization, and show potential to balance between accuracy and generalization, although the parameters used in the model have been remarkably reduced. These models based on quantum hybrid deep learning (QDL) show robust predictions on four benchmark datasets, and exhibit the practical application power in drug screening for targets related to human liver cirrhosis. This work highlights the potential of the hybrid quantum deep learning algorithm in solving complex problems in bioinformatics.

Quantum-to-Classical Neural Network Transfer Learning Applied to Drug Toxicity Prediction

“…Leveraging the principles of superposition and entanglement, quantum computing offers a new framework that potentially expands computational boundaries for ML. Recent work suggests that quantum ML (QML) models are more learnable and generalize better to unseen data than classical networks. − In pursuit of these potential advantages, researchers have built numerous QML models to address a range of chemical and biological problems. − More specifically, there have been several QML works that are trained to predict toxicity. ,, Despite the success of these QML models, the limitations that plague noisy intermediate-scale quantum (NISQ) devices, such as decoherence and gate errors, are still a sobering reality.…”