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Journal of Computer-Aided Molecular Design

Quantum algorithm for protein-ligand docking sites identification in the interaction space

Ioannis Liliopoulos, Georgios D. Varsamis, Theodora Karamanidou, Christos Papalitsas, Grigorios Koulouras, Vassilios Pantazopoulos, Thanos G. Stavropoulos, Ioannis G. Karafyllidis

Published Jul 4, 2025• 5 citations
Publisher

Abstract

Over the past two decades, the development of novel drugs evolved into a high-demanding computational field. There is a constant and increasing need for advanced methods for determining protein-ligand binding in the drug design process. Even after the introduction and use of High-Performance Computers in drug design, fundamental problems and constraints have not been dealt with in a satisfactory manner. This is partially due to the fact that ligand docking in proteins is a quantum mechanical process. With the quantum computers available today, the question "Can quantum computers be used in drug design and how?" arises naturally. A novel quantum algorithm for protein-ligand docking site identification is presented here. In detail, the protein lattice model has been expanded to include protein-ligand interactions. Quantum state labelling for the interaction sites is introduced, and an extended and modified Grover quantum search algorithm is implemented to search for docking sites. This algorithm has been tested and executed on both a quantum simulator and a real quantum computer. The results show that the quantum algorithm can identify effectively docking sites. The quantum algorithm is highly scalable and well-suited for identifying docking sites within large proteins, poised to harness the potential of increased quantum bits in the future.

DOI
10.1007/s10822-025-00620-5

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Over the past two decades, the development of novel drugs evolved into a high-demanding computational field. There is a constant and increasing need for advanced methods for determining protein-ligand binding in the drug design process. Even after the introduction and use of High-Performance Computers in drug design, fundamental problems and constraints have not been dealt with in a satisfactory manner. This is partially due to the fact that ligand docking in proteins is a quantum mechanical process. With the quantum computers available today, the question "Can quantum computers be used in drug design and how?" arises naturally. A novel quantum algorithm for protein-ligand docking site identification is presented here. In detail, the protein lattice model has been expanded to include protein-ligand interactions. Quantum state labelling for the interaction sites is introduced, and an extended and modified Grover quantum search algorithm is implemented to search for docking sites. This algorithm has been tested and executed on both a quantum simulator and a real quantum computer. The results show that the quantum algorithm can identify effectively docking sites. The quantum algorithm is highly scalable and well-suited for identifying docking sites within large proteins, poised to harness the potential of increased quantum bits in the future.
A study published in the Journal of Computer-Aided Molecular Design presents the first innovative quantum algorithm for identifying the optimal docking sites where proteins and ligands bind during the drug design process. It expands the existing protein lattice model to fully include hydrogen bonding and hydrophobic interactions, introducing quantum state labelling for interaction sites and applying an extended and modified Grover quantum search algorithm. The algorithm was successfully verified on IBM's real 127-qubit quantum computers by applying error mitigation techniques (such as M3 and Pauli twirling), and it is expected to exponentially accelerate drug candidate screening for large proteins as hardware scales up in the future. [Quantum Biology Society] Even after the introduction of High-Performance Computers (HPC) into drug development, protein-ligand docking has faced fundamental computational limits with classical computing methods because it is inherently a complex quantum mechanical interaction process. In response to the question, "Can quantum computers be used in drug design and how?" raised alongside the rapid advancement of quantum computers today, a study titled "Quantum algorithm for protein-ligand docking sites identification in the interaction space," recently published in the international journal Journal of Computer-Aided Molecular Design, has provided a clear answer. An industry-academia joint research team consisting of Professor Ioannis G. Karafyllidis from the Democritus University of Thrace in Greece and researchers from the multinational pharmaceutical company Pfizer conducted this innovative study. ■ Combination of an Extended Lattice Model and Grover's Quantum Search Algorithm The researchers significantly expanded the previously used protein lattice model to fully encompass hydrogen bonding and hydrophobic interactions, which are key interactions between proteins and ligands. Within this expanded model, they introduced quantum state labelling for each interaction site and implemented an extended and modified form of Grover's quantum search algorithm. Simulation of the algorithm demonstrated that it can identify potential docking sites inside the target protein even within a massive interaction space, and evaluate the optimal binding positions much more efficiently and rapidly than classical computing tools through Euclidean distance calculations based on the quantum SWAP test. ■ Real Quantum Hardware Verification and Infinite Scalability Notably, this study did not stop at simulators but verified the algorithm's accuracy by running it directly on IBM's real 127-qubit quantum computers (such as ibm_brisbane). To overcome the hardware noise generated during this process, they successfully applied state-of-the-art quantum error mitigation techniques such as M3 (Matrix-free Measurement Mitigation), Pauli twirling, and dynamical decoupling. The most powerful asset of the newly developed quantum algorithm is its exceptional scalability. Thanks to this characteristic, the algorithm is perfectly suited for exploring binding sites within complex target proteins with massive molecular weights. By breaking the bottleneck of traditional drug discovery pipelines that required heavy computations, it is expected to serve as a revolutionary milestone that will exponentially explode the potential of drug candidate screening and molecular design as the number of available qubits increases with future hardware advancements. #QuantumAlgorithm #DrugScreening #ProteinLigandDocking #GroverAlgorithm #QuantumComputing #DrugDevelopment #MolecularDocking #QuantumBiology #Biophysics #KoreanQuantumBiologySociety https://www.researchgate.net/publication/393436574_Quantum_algorithm_for_protein-ligand_docking_sites_identification_in_the_interaction_space
The large search space and the challenge of accurately simulating quantum-level molecular interactions are the fundamental causes of the delayed, complicated, and costly process of discovering new medications. The effectiveness of conventional computational techniques, including molecular docking and molecular dynamics, to assess molecular characteristics and provide highly accurate drug candidate predictions is constrained. This paper presents a Quantum-Assisted Drug Discovery Simulation framework that combines cutting-edge quantum computing algorithms, specifically the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE), with traditional molecular docking tools like RDKit and AutoDock. The system uses these quantum algorithms to perform very accurate Quantum Refinement on the most potential therapeutic protein poses found by classical identification. This hybrid approach seeks to optimize drug-protein interactions more quickly, speed up early-stage drug discovery, and create opportunities for less costly, more accurate medical research, even though a combination of quantum and classical calculations allows more accurate prediction of molecular energies and binding affinities.
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.
Research Problem: The traditional methods of drug discovery are time-consuming, costly, and at times ineffective. There is a need for more novel approaches that can enhance the processes of discovering new drugs and developing them into usable products. Objective of the Paper: This paper aims to conduct a review of the literature and find how the application of quantum computing can change the way drugs are discovered and processed in practice. Process of Review: The review process involved a comprehensive examination of the available literature and recent advances in the application of quantum computing to drug discovery. The review encompassed major studies, theories, and case studies related to the subject matter, aiming to identify the current state and potential future directions of the domain. Insights of the Review: 1. Primary Building Blocks: With quantum computing, superposition, entanglement, and other principles can be utilised to enable complex computations to be performed at higher speeds using less time than traditional computers. 2. Quantum modelling: Quantum computers are expected to accurately model the interactions of various molecules, which could result in the identification of viable drug candidates. 3. Drug Discovery Processes: Quantum algorithms are capable of faster processing than traditional methods when it comes to solving optimisation problems, such as protein structure prediction and molecular docking, in drug design. 4. Limitations and Challenges: As great as the promise of quantum computing is, it has its limitations and challenges, including hardware limitations, error rates, and the need for specialised algorithms. 5. Future Developments: The ongoing development of quantum hardware and algorithms is expected to enhance the practical applications of quantum computing in drug discovery, enabling the quicker and more cost-effective creation of new drugs.
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.
In this paper, we present a novel approach to protein folding and drug discovery leveraging Grover’s algorithm. Our protein folding methodology focuses on accurately determining the phi and psi angles to achieve optimal protein configurations, ensuring the system attains a low energy state. In drug discovery, our algorithm accommodates variable primary structures, incorporating a critical examination step for comprehensive analysis. Detailed simulations demonstrate the efficacy of our approach in identifying potential drug candidates with high accuracy. Enhancements to Grover’s algorithm are realized through the integration of the Assembly-ToQuantumCompiler, optimizing quantum circuit designs for increased efficiency. Additionally, we introduce the Quantum Binary Neural Network (QBNN) to improve search precision and computational performance. By combining quantum algorithms with advanced compilers and neural networks, we aim to revolutionize computational biology and accelerate drug discovery. These algorithms improve as quantum computers improve.