A Critical Analysis of Quantum Machine Learning in Preclinical Drug Development: Opportunities and Challenges

Abstract

Quantum machine learning (QML) has recently emerged as a promising approach for enhancing various stages of preclinical drug development. QML utilizes the principles of quantum mechanics to develop more powerful and efficient machine learning models compared to classical techniques. This paper provides a comprehensive critical analysis of the applications, merits, limitations of QML across key aspects of preclinical drug discovery - target identification and validation, lead generation and optimization, ADME/Tox prediction. The exponential speed-up promised by QML algorithms could potentially transform structure-activity relationship studies, molecular dynamic simulations, and protein folding predictions. However, challenges remain due to the inherent noise and errors in near-term quantum devices. The lack of large, high-quality pharmaceutical datasets and absence of robust evaluation metrics is another bottleneck. The paper highlights best practices and open problems in applying QML for accelerating preclinical workflows in a noise-aware, data-efficient, and trustworthy manner. Broader regulatory and ethical implications are also discussed to facilitate responsible adoption of QML in drug development. This timely and rigorous analysis will equip researchers and industry practitioners with a nuanced perspective on harnessing QML for advancing pharmaceutical innovations and expediting therapeutic breakthroughs.