Hybrid Quantum Neural Network Approaches to Protein–Ligand Binding Affinity Prediction

Maria Avramouli (), Ilias K. Savvas, Anna Vasilaki, Andreas Tsipourlianos and Georgia Garani
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Maria Avramouli: Department of Digital Systems, University of Thessaly, GAIOPOLIS, 41500 Larissa, Greece
Ilias K. Savvas: Department of Digital Systems, University of Thessaly, GAIOPOLIS, 41500 Larissa, Greece
Anna Vasilaki: Laboratory of Pharmacology, Faculty of Medicine, University of Thessaly, BIOPOLIS, 41500 Larissa, Greece
Andreas Tsipourlianos: Department of Digital Systems, University of Thessaly, GAIOPOLIS, 41500 Larissa, Greece
Georgia Garani: Department of Digital Systems, University of Thessaly, GAIOPOLIS, 41500 Larissa, Greece

Mathematics, 2024, vol. 12, issue 15, 1-24

Abstract: Drug repositioning is a less expensive and time-consuming method than the traditional method of drug discovery. It is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication. A key strategy in repositioning approved or investigational drugs is determining the binding affinity of these drugs to target proteins. The large increase in available experimental data has helped deep learning methods to demonstrate superior performance compared to conventional prediction and other traditional computational methods in precise binding affinity prediction. However, these methods are complex and time-consuming, presenting a significant barrier to their development and practical application. In this context, quantum computing (QC) and quantum machine learning (QML) theoretically offer promising solutions to effectively address these challenges. In this work, we introduce a hybrid quantum–classical framework to predict binding affinity. Our approach involves, initially, the implementation of an efficient classical model using convolutional neural networks (CNNs) for feature extraction and three fully connected layers for prediction. Subsequently, retaining the classical module for feature extraction, we implement various quantum and classical modules for binding affinity prediction, which accept the concatenated features as input. Quantum predicted modules are implemented with Variational Quantum Regressions (VQRs), while classical predicted modules are implemented with various fully connected layers. Our findings clearly show that hybrid quantum–classical models accelerate the training process in terms of epochs and achieve faster stabilization. Also, these models demonstrate quantum superiority in terms of complexity, accuracy, and generalization, thereby indicating a promising direction for QML.

Keywords: quantum machine learning; quantum computing; protein–ligand binding affinity; quantum neural network; quantum regression (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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