Quantum Approximate Optimization Algorithms for Molecular Docking

2025-03-06

AI Summary

This work addresses the computational challenge of identifying optimal ligand–protein binding conformations in molecular docking. We propose a hybrid classical–quantum optimization method, introducing for the first time the Digitized Counterdiabatic Quantum Approximate Optimization Algorithm (DC-QAOA) to molecular docking. By integrating digitized counterdiabatic driving, our approach enhances QAOA’s optimization capability and convergence robustness. The algorithm is simulated on a GPU cluster, successfully solving instances with up to 14 out of 17 qubits—surpassing the current publicly reported maximum of 12 qubits. Numerical validation confirms that the obtained binding conformations closely match theoretical ground-truth solutions. While the method demonstrates scalability in problem size, computational time increases significantly with qubit count. This study establishes a novel paradigm and provides empirical evidence for quantum-inspired algorithms in accelerating drug discovery.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Quantum Machine Learning
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Addressing molecular docking complexity in drug discovery.
• Applying hybrid classical-quantum computing to optimize docking.
• Evaluating DC-QAOA on large biomolecular systems.

Innovation

• Hybrid classical-quantum computing techniques
• Digitized Counterdiabatic QAOA approach
• Simulated quantum runs on GPU cluster