Quantum Convolution for Structure-Based Virtual Screening

• URL: http://arxiv.org/abs/2507.09667v1
• Date: Sun, 13 Jul 2025 15:03:43 GMT
• Title: Quantum Convolution for Structure-Based Virtual Screening
• Abstract summary: Structure-based virtual screening (SBVS) is a key computational strategy for identifying potential drug candidates.<n>This study proposes a quantum convolutional neural network (QCNN) framework to estimate delta G_bind efficiently.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Classical and Hybrid Quantum Machine Learning for Trigger-Like Event Selection on CMS Open Data: An Eight-Qubit, PCA-Constrained Benchmark [0.9106818059692908]
  Event triggering sits at the heart of high-energy physics, where rare events of interest must be retained.<n>This work compares four classical machine learning models, namely a support vector machine, an artificial neural network, a convolutional network and a long short-term memory network.<n>Four hybrid quantum counterparts, on a trigger-like binary classification task built from CMS open data.
  arXiv  Detail & Related papers  (2026-08-26T15:32:44Z)
• Quantum Machine Learning for particle scattering entanglement classification [0.0]
  Entanglement is a key quantity for characterizing quantum correlations in particle scattering processes.<n>We investigate whether fermion density profiles can serve as proxies for entanglement by framing the problem as a classification task.<n>We find that Quantum Convolutional Neural Networks (QCNNs) achieve consistently competitive or superior accuracy with faster convergence and lower variance.
  arXiv  Detail & Related papers  (2026-04-07T15:13:38Z)
• Parallel Multi-Circuit Quantum Feature Fusion in Hybrid Quantum-Classical Convolutional Neural Networks for Breast Tumor Classification [0.0]
  We present a hybrid Quantum-Classical Convolutional Neural Network (QCNN) architecture designed for the binary classification of the BreastMNIST dataset.<n>Our results indicate that hybrid QCNN architectures can leverage entanglement and quantum feature fusion to enhance medical image classification tasks.
  arXiv  Detail & Related papers  (2025-11-29T17:47:14Z)
• Quantum Machine Learning for UAV Swarm Intrusion Detection [25.52804434998647]
  Intrusion detection inUAV swarms is complicated by high mobility, non-stationary traffic, and severe class imbalance.<n>We benchmark three quantum-machine-learning (QML) approaches - quantum kernels, variational quantum neural networks (QNNs), and hybrid quantum-trained neural networks (QT-NNs)<n>All models consume an 8-feature flow representation and are evaluated under identical preprocessing, balancing, and noise-model assumptions.<n>Results reveal clear trade-offs: quantum kernels and QT-NNs excel in low-data, nonlinear regimes, while deeper QNNs suffer from trainability issues, and CNN
  arXiv  Detail & Related papers  (2025-09-01T22:36:30Z)
• TensoMeta-VQC: A Tensor-Train-Guided Meta-Learning Framework for Robust and Scalable Variational Quantum Computing [60.996803677584424]
  TensoMeta-VQC is a novel tensor-train (TT)-guided meta-learning framework designed to improve the robustness and scalability of VQC significantly.<n>Our framework fully delegates the generation of quantum circuit parameters to a classical TT network, effectively decoupling optimization from quantum hardware.
  arXiv  Detail & Related papers  (2025-08-01T23:37:55Z)
• Quantum Machine Learning for Predicting Binding Free Energies in Structure-Based Virtual Screening [0.0]
  We introduce a quantum machine learning approach that encodes molecular information into quantum states.<n>With six quantum circuit units, the model achieves an RMSD of 2.37 kcal/mol and a Pearson correlation of 0.650.
  arXiv  Detail & Related papers  (2025-07-24T14:01:39Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• An Efficient Quantum Classifier Based on Hamiltonian Representations [50.467930253994155]
  Quantum machine learning (QML) is a discipline that seeks to transfer the advantages of quantum computing to data-driven tasks.<n>We propose an efficient approach that circumvents the costs associated with data encoding by mapping inputs to a finite set of Pauli strings.<n>We evaluate our approach on text and image classification tasks, against well-established classical and quantum models.
  arXiv  Detail & Related papers  (2025-04-13T11:49:53Z)
• Hybrid Quantum Neural Networks with Variational Quantum Regressor for Enhancing QSPR Modeling of CO2-Capturing Amine [0.9968037829925945]
  We develop hybrid quantum neural networks (HQNN) to improve structure-property relationship modeling for CO2-capturing amines.<n>HQNNs improve predictive accuracy for key solvent properties, including basicity, viscosity, boiling point, melting point, and vapor pressure.
  arXiv  Detail & Related papers  (2025-03-01T07:26:45Z)
• BenchQC: A Benchmarking Toolkit for Quantum Computation [0.4037357056611557]
  Variational Quantum Eigensolver (VQE) is a promising algorithm for quantum computing applications in chemistry and materials science.<n>This study benchmarks the performance of the VQE for calculating ground-state energies of aluminum clusters.
  arXiv  Detail & Related papers  (2025-02-13T18:51:08Z)
• Benchmarking Quantum Convolutional Neural Networks for Classification and Data Compression Tasks [0.4379805041989628]
  Quantum Convolutional Neural Networks (QCNNs) have emerged as promising models for quantum machine learning tasks. This paper investigates the performance of QCNNs in comparison to the hardware-efficient ansatz (HEA) for classifying the phases of quantum ground states.
  arXiv  Detail & Related papers  (2024-11-20T17:17:09Z)
• Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance [67.77677387243135]
  Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML) At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation.
  arXiv  Detail & Related papers  (2024-11-13T23:56:20Z)
• Memory-Augmented Hybrid Quantum Reservoir Computing [0.0]
  We present a hybrid quantum-classical approach that implements memory through classical post-processing of quantum measurements. We tested our model on two physical platforms: a fully connected Ising model and a Rydberg atom array.
  arXiv  Detail & Related papers  (2024-09-15T22:44:09Z)
• Importance sampling for stochastic quantum simulations [68.8204255655161]
  We introduce the qDrift protocol, which builds random product formulas by sampling from the Hamiltonian according to the coefficients. We show that the simulation cost can be reduced while achieving the same accuracy, by considering the individual simulation cost during the sampling stage. Results are confirmed by numerical simulations performed on a lattice nuclear effective field theory.
  arXiv  Detail & Related papers  (2022-12-12T15:06:32Z)
• Toward Trainability of Quantum Neural Networks [87.04438831673063]
  Quantum Neural Networks (QNNs) have been proposed as generalizations of classical neural networks to achieve the quantum speed-up. Serious bottlenecks exist for training QNNs due to the vanishing with gradient rate exponential to the input qubit number. We show that QNNs with tree tensor and step controlled structures for the application of binary classification. Simulations show faster convergent rates and better accuracy compared to QNNs with random structures.
  arXiv  Detail & Related papers  (2020-11-12T08:32:04Z)
• On the learnability of quantum neural networks [132.1981461292324]
  We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme. We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
  arXiv  Detail & Related papers  (2020-07-24T06:34:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.