Future Prospects of Quantum Machine Learning in Accelerating Drug Discovery Processes

Abstract

Drug discovery is a complex, lengthy and expensive process with high failure rates. Advances in quantum computing and quantum machine learning have the potential to significantly accelerate and improve various aspects of drug discovery. This paper reviews the current state of quantum machine learning, its relevance to drug discovery and the future prospects of using quantum techniques to expedite and enhance drug discovery workflows. The key application areas explored include target identification and validation, molecular design and lead optimization, preclinical studies and clinical trials. The quantum algorithms best suited for drug discovery tasks are analyzed along with the hardware requirements. The limitations and challenges of applying quantum techniques are also discussed. Three tables summarize the quantum machine learning methods, quantum hardware platforms and the timeline for realizing quantum advantage in pharmaceutical research. Overall, quantum machine learning holds immense promise in revolutionizing computational drug discovery in the future. However, significant progress is still needed in developing practical quantum algorithms, assembling large benchmark datasets and building fault-tolerant quantum computers. A prudent strategy would be to focus quantum software development on use cases with an achievable quantum speedup on near-term intermediate-scale quantum processors. Multi-stakeholder collaboration between pharmaceutical companies, quantum hardware vendors, researchers and regulators is crucial to successfully harness the power of quantum computing for advanced drug discovery.