Quantum Convolution for Structure-Based Virtual Screening

TL;DR

This paper introduces a quantum convolutional neural network framework for efficient structure-based virtual screening, demonstrating promising results and robustness against quantum noise in estimating protein-ligand binding energies.

Contribution

It presents the first application of quantum CNNs to predict binding free energies, showing feasibility and noise tolerance for drug discovery tasks.

Findings

Achieved a Pearson correlation of 0.694 on test data.

Quantum noise increased RMSD but maintained correlation stability.

Demonstrated potential for quantum computing in high-throughput virtual screening.

Abstract

Structure-based virtual screening (SBVS) is a key computational strategy for identifying potential drug candidates by estimating the binding free energies (delta G_bind) of protein-ligand complexes. The immense size of chemical libraries, combined with the need to account for protein and ligand conformations as well as ligand translations and rotations, makes these tasks computationally intensive on classical hardware. This study proposes a quantum convolutional neural network (QCNN) framework to estimate delta G_bind efficiently. Using the PDBbind v2020 dataset, we trained QCNN models with 9 and 12 qubits, with the core set designated as the test set. The best-performing model achieved a Pearson correlation coefficient of 0.694 on the test set. To assess robustness, we introduced quantum noise under two configurations. While noise increased the root mean square deviation, the Pearson correlation coefficient remained largely stable. These results demonstrate the feasibility and noise tolerance of QCNNs for high-throughput virtual screening and highlight the potential of quantum computing to accelerate drug discovery.