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arXiv:2503.04239

Quantum Approximate Optimization Algorithms for Molecular Docking

Christos Papalitsas, Yanfei Guan, Shreyas Waghe, Athanasios Liakos, Ioannis Balatsos, Vassilios Pantazopoulos

Published Mar 6, 2025• 2 citations
arXivHTMLPDF

Abstract

Molecular docking is a critical process for drug discovery and challenging due to the complexity and size of biomolecular systems, where the optimal binding configuration of a drug to a target protein is determined. Hybrid classical-quantum computing techniques offer a novel approach to address these challenges. The Quantum Approximate Optimization Algorithm (QAOA) and its variations are hybrid classical-quantum techniques, and a promising tool for combinatorial optimization challenges. This paper presents a Digitized Counterdiabatic QAOA (DC-QAOA) approach to molecular docking. Simulated quantum runs were conducted on a GPU cluster. We examined 14 and 17 nodes instances - to the best of our knowledge the biggest published instance is 12-node at Ding et al. and we present the results. Based on computational results, we conclude that binding interactions represent the anticipated exact solution. Additionally, as the size of the examined instance increases, the computational times exhibit a significant escalation.

DOI
10.48550/arxiv.2503.04239

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Molecular docking is a critical process for drug discovery and challenging due to the complexity and size of biomolecular systems, where the optimal binding configuration of a drug to a target protein is determined. Hybrid classical-quantum computing techniques offer a novel approach to address these challenges. The Quantum Approximate Optimization Algorithm (QAOA) and its variations are hybrid classical-quantum techniques, and a promising tool for combinatorial optimization challenges. This paper presents a Digitized Counterdiabatic QAOA (DC-QAOA) approach to molecular docking. Simulated quantum runs were conducted on a GPU cluster. We examined 14 and 17 nodes instances - to the best of our knowledge the biggest published instance is 12-node at Ding et al. and we present the results. Based on computational results, we conclude that binding interactions represent the anticipated exact solution. Additionally, as the size of the examined instance increases, the computational times exhibit a significant escalation.
Molecular docking plays a pivotal role in drug discovery and precision medicine, furnishing insights into protein functionalities and fostering the development of therapeutics. Here, we introduce a potential alternative solution to this problem using a quantum approximate optimization algorithm (QAOA) based algorithm. Our method was applied to analyze diverse biological systems, including the SARS-CoV-2 Mpro{\mathrm{M}}^{\text{pro}}Mpro complex with PM-2-020B, the DPP-4 complex with piperidine fused imidazopyridine 34, and the HIV-1 gp120 complex with JP-III-048. We found that the digitized-counterdiabatic QAOA (DCQAOA), which integrates the concept of counterdiabatic driving, surpasses the conventional QAOA in terms of quantum circuit depth and optimization efficiency. This is particularly evident in complex molecular-docking challenges, where DCQAOA delivers more accurate and biologically relevant results. Our research highlights the promising role of quantum computing in drug discovery and contributes significant insights towards the optimization of protein-ligand docking methodologies.
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Molecular docking, a critical task in drug discovery, represents a highly promising application domain for quantum computing. While the Quantum Approximate Optimization Algorithm (QAOA) shows potential, its performance on near-term quantum devices is limited by noise susceptibility and optimization challenges such as barren plateaus. To address these limitations, we apply Adaptive Bias Fields QAOA (ABF-QAOA) to solve molecular docking problems mapped to Ising Hamiltonians. Our results demonstrate that ABF-QAOA achieves accelerated convergence and higher solution accuracy compared to standard QAOA. In addition, under simulated quantum noise conditions, ABF-QAOA maintains higher solution quality, showcasing significantly enhanced noise resilience. This work establishes ABF-QAOA as a robust quantum approach for molecular docking on noisy intermediate-scale quantum (NISQ) hardware.
Molecular docking predicts how a small molecule binds to a protein and is a key bottleneck in drug discovery. Classical scoring functions sum empirical pairwise contacts, blind to quantum-mechanical effects like orbital charge transfer that govern binding specificity. We introduce Q-Score, encoding GNN-predicted orbital donor-acceptor energies into a weighted graph and scoring binding by solving a maximum-weight vertex clique problem via Digitized-Counterdiabatic QAOA. Each interaction anchor maps to one qubit and compatibility constraints become edges. Across 11 protein targets, DC-QAOA recovers the exact optimum on 8 at 10 qubits. On 1000 AI-generated molecules, Q-Score is orthogonal to classical scoring with Spearman rho of 0.05, driven by orbital quality with rho of 0.90, and free of molecular-weight bias, enriching for strong orbital interactions at twice the random rate. DC-QAOA achieves a mean approximation ratio of 0.94 with 52 percent exact. Execution of 1000 circuits on IBM Eagle confirms 6-qubit solvability on NISQ hardware.
Quantum computing has emerged as a promising paradigm for solving computationally intractable problems in healthcare, particularly within the scope of precision medicine. Traditional approaches to drug discovery, genomic analysis, and healthcare optimisation face significant limitations due to the exponential complexity of biological and clinical datasets. This article discusses the use of the Quantum Approximate Optimisation Algorithm (QAOA) as a quantum-classical hybrid framework to solve these issues. In drug discovery, QAOA provided better binding affinity prediction and lower root mean square deviation (RMSD) than traditional docking techniques. In genomic analysis, QAOA-boosted feature selection enhanced classification accuracy at the cost of reduced convergence time. Likewise, QAOA-based scheduling yielded better performance than traditional heuristics in healthcare optimisation by providing greater resource usage with fewer iterations. Experimental results show that warm-starting QAOA with classical initialisation reduces convergence further by up to 30%. These results highlight the promise of QAOA in speeding up biomedical computations and pave the way for scalable, quantum-enabled solutions in precision medicine.
With the continuous growth of quantum hardware and its increasing accessibility, there is a shift away from theoretical research towards real-world problems. One of the most promising applications of quantum computing is drug discovery, which is a complex and multi-disciplinary process. To increase the success probability of finding new drugs, we can introduce an in-silico step to virtual screen the drug candidates. In this paper, we address the molecular docking problem of virtual screening. In particular, we analyze the performance of a molecular docking application, which is modeled as a Quadratic Unconstrained Binary Optimization (QUBO) instance. We leveraged the computational power offered by different quantum computing models, gate-based and quantum annealing, to speed the solution of the QUBO formulation. Experimental results show how quantum annealer is still the best in class for our case study. Moreover, we report here the experience and the takeaways we obtained by executing quantum molecular docking on publicly accessible quantum hardware and software.