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arXiv:2308.04098 · Physical Review Applied

Molecular docking via quantum approximate optimization algorithm

Qi-Ming Ding, Yiming Huang, Xiao Yuan

Published Mar 19, 2024• 45 citations
arXivHTMLPDF

Abstract

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 ${\mathrm{M}}^{\text{pro}}$ 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.

DOI
10.1103/physrevapplied.21.034036

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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.
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Molecular docking and compound screening can overlook and biochemically relevant binding configurations of molecules, limiting the ability to identify true functional poses of potential drug candidates. To address this, we apply the Quantum Approximate Optimization Algorithm (QAOA) to improve the evaluation of target–ligand interaction patterns. As a proof of concept, we focus on the CDC45–MCM2-7–GINS (CMG) helicase, which is essential for DNA replication and represents a potential therapeutic target in cancer due to mutant KRas-induced vulnerabilities in the CMG and its MCM hexameric core. In this study, docking of three ligands: methylbiocin, novobiocin, and BL_C43, to one of the ATP-binding regions of the CMG was formulated as a combinatorial optimization problem. Pharmacophore interactions were encoded in a Binding Interaction Graph and mapped to a Maximum Independent Set problem, allowing exploration of all feasible docking configurations. The quantum workflow identified ligand-dependent interaction patterns and biologically relevant binding orientations that classical docking methods failed to predict, demonstrating superior agreement with experimental biochemical outcomes for CMG inhibitors. These findings establish QAOA as a promising approach for overcoming limitations of classical molecular docking and advancing the rational design of improved inhibitors targeting CMG and other cancer-associated proteins.
Accurate prediction of binding affinities and efficient lead optimization remain central challenges in computational drug discovery, particularly for complex biological targets characterized by rugged energy landscapes, conformational heterogeneity, and high-dimensional interaction spaces. Classical computational methods—such as molecular docking, force-field–based simulations, and deep learning models—have made substantial progress, yet they often struggle to capture quantum-mechanical effects and long-range correlations critical to molecular recognition. This study explores a quantum-enhanced framework that integrates Variational Quantum Algorithms (VQAs), quantum-inspired Hamiltonian modeling, and hybrid quantum–classical optimization workflows to improve the fidelity and scalability of drug discovery pipelines. By encoding molecular interactions into parameterized quantum circuits, the framework leverages quantum superposition and entanglement to explore complex chemical space more efficiently, enabling refined estimation of binding energies and accelerated identification of high-quality leads. Preliminary simulations indicate that VQA-based estimators outperform classical baselines for challenging protein–ligand systems, reducing prediction error while maintaining computational tractability. These findings highlight the emerging potential of quantum technologies to enable more accurate, data-efficient, and mechanistically grounded drug discovery for next-generation therapeutics.