Practical protein-pocket hydration-site prediction for drug discovery on a quantum computer

• URL: http://arxiv.org/abs/2512.08390v2
• Date: Thu, 11 Dec 2025 10:53:52 GMT
• Title: Practical protein-pocket hydration-site prediction for drug discovery on a quantum computer
• Authors: Daniele Loco, Kisa Barkemeyer, Andre R. R. Carvalho, Jean-Philip Piquemal,
• Abstract summary: We show how to perform three-dimensional protein pockets hydration-site prediction on a quantum computer.<n>Our results reproduced experimental predictions on real-life protein-ligand complexes.<n>The method has potential for assisting simulations of protein-ligand complexes for drug lead optimization and setup of docking calculations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Demonstrating the practical utility of Noisy Intermediate-Scale Quantum (NISQ) hardware for recurrent tasks in Computer-Aided Drug Discovery is of paramount importance. We tackle this challenge by performing three-dimensional protein pockets hydration-site prediction on a quantum computer. Formulating the water placement problem as a Quadratic Unconstrained Binary Optimization (QUBO), we use a hybrid approach coupling a classical three-dimensional reference-interaction site model (3D-RISM) to an efficient quantum optimization solver, to run various hardware experiments up to 123 qubits. Matching the precision of classical approaches, our results reproduced experimental predictions on real-life protein-ligand complexes. Furthermore, through a detailed resource estimation analysis, we show that accuracy can be systematically improved with increasing number of qubits, indicating that full quantum utility is in reach. Finally, we provide evidence that advantageous situations could be found for systems where classical optimization struggles to provide optimal solutions. The method has potential for assisting simulations of protein-ligand complexes for drug lead optimization and setup of docking calculations.

Related papers

• Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits [48.33631905972908]
  We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC) This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions. Our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach.
  arXiv  Detail & Related papers  (2024-11-13T12:03:39Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [62.46800898243033]
  Recent progress in quantum learning theory prompts a question: can linear properties of a large-qubit circuit be efficiently learned from measurement data generated by varying classical inputs?<n>We prove that the sample complexity scaling linearly in $d$ is required to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.<n>We propose a kernel-based method leveraging classical shadows and truncated trigonometric expansions, enabling a controllable trade-off between prediction accuracy and computational overhead.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
  Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems. In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems. We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
  arXiv  Detail & Related papers  (2024-04-17T11:00:12Z)
• Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing [93.83016310295804]
  AQCs allow to implement problems of research interest, which has sparked the development of quantum representations for computer vision tasks. In this work, we explore the potential of using this information for probabilistic balanced k-means clustering. Instead of discarding non-optimal solutions, we propose to use them to compute calibrated posterior probabilities with little additional compute cost. This allows us to identify ambiguous solutions and data points, which we demonstrate on a D-Wave AQC on synthetic tasks and real visual data.
  arXiv  Detail & Related papers  (2023-10-18T17:59:45Z)
• Exploring the Potential of Qutrits for Quantum Optimization of Graph Coloring [0.0]
  Qutrits may be useful in solving some problems on near-term devices. We run noiseless simulations using PennyLane to compare the formulation against qubit-based QAOA. This work suggests that qutrits may be useful in solving some problems on near-term devices, however further work is required to assess their potential in a noisy environment.
  arXiv  Detail & Related papers  (2023-08-15T21:31:37Z)
• Molecular docking via quantum approximate optimization algorithm [1.2406403248205282]
  Molecular docking plays a pivotal role in drug discovery and precision medicine, enabling us to understand protein functions and advance novel therapeutics. Here, we introduce a potential alternative solution to this problem, the digitized-counterdiabatic quantum approximate optimization algorithm (DC-QAOA), which utilizes counterdiabatic driving and QAOA on a quantum computer. Our findings underscore quantum computing's potential in drug discovery and offer valuable insights for optimizing protein-ligand docking processes.
  arXiv  Detail & Related papers  (2023-08-08T07:25:34Z)
• Quantum topology optimization of ground structures using noisy intermediate-scale quantum devices [8.325359814939517]
  We study the usage of quantum computers as a potential solution to topology optimization problems. Several experiments, including a real device experiment, show that the proposed method successfully obtained the optimal configurations.
  arXiv  Detail & Related papers  (2022-07-19T10:39:28Z)
• Optimization of Sensor-Placement on Vehicles using Quantum-Classical Hybrid Methods [0.923687371636986]
  Quantum Computers are expected to be able to solve certain optimization problems more "easily" in the future. This paper presents two formulations for quantum-enhanced solutions in a systematic manner.
  arXiv  Detail & Related papers  (2022-06-29T11:49:28Z)
• Adiabatic Quantum Computing for Multi Object Tracking [170.8716555363907]
  Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time. As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware. We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers.
  arXiv  Detail & Related papers  (2022-02-17T18:59:20Z)
• A Hybrid Quantum-Classical Algorithm for Robust Fitting [47.42391857319388]
  We propose a hybrid quantum-classical algorithm for robust fitting. Our core contribution is a novel robust fitting formulation that solves a sequence of integer programs. We present results obtained using an actual quantum computer.
  arXiv  Detail & Related papers  (2022-01-25T05:59:24Z)
• Quantum variational PDE solver with machine learning [0.0]
  We propose a quantum variational (QuVa) PDE solver with the aid of machine learning (ML) schemes. The core quantum processing in this solver is to calculate efficiently the expectation value of specially designed quantum operators.
  arXiv  Detail & Related papers  (2021-09-19T20:30:02Z)
• Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
  We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems. By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
  arXiv  Detail & Related papers  (2021-08-03T17:49:09Z)

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.