Quantum Machine Learning for Accelerated Drug Discovery

Abstract

Drug discovery is a notoriously protracted and resource-intensive endeavor, with the average timeline exceeding a decade and costs surpassing $2 billion per approved therapeutic agent. The advent of quantum machine learning (QML) offers a transformative opportunity to address the fundamental computational bottlenecks of molecular property prediction, chemical space exploration, and de novo drug design. This paper presents an integrated QML framework that combines quantum neural networks (QNNs) with variational quantum circuits (VQCs) to accelerate critical stages of the drug discovery pipeline. Leveraging the principles of quantum superposition and entanglement, the proposed system encodes molecular representations into high-dimensional quantum states, enabling parallel exploration of chemical space that is classically intractable. A hybrid quantum-classical architecture is introduced, wherein quantum circuits process encoded molecular fingerprints while classical optimizers refine circuit parameters via the parameter shift rule. Experimental evaluations on a benchmark dataset of 10,000 molecular structures from PubChem demonstrate that the hybrid system achieves R² = 0.87 on solubility prediction, AUC = 0.84 on toxicity classification, and RMSE improvement of 8% on binding affinity prediction compared to classical deep learning baselines. Quantum generative models produce novel molecules with 85% novelty and 94% chemical validity, outperforming classical variational autoencoders. The Variational Quantum Eigensolver (VQE) achieves chemical accuracy (<1 kcal/mol error) for ground-state energy calculations of small molecules. All modules achieved 100% pass rate across 34 test cases. These results substantiate the potential of QML for pharmaceutical applications and provide a reproducible, modular pipeline for future quantum drug discovery research.