Drug discovery pipelines are often time-consuming, expensive, and challenging at each level of the workflow for validation of potential therapeutic targets and prediction of potential hit molecules before final approval as new drugs.(p1) Traditionally, this process takes over a decade and costs billions of dollars, with many drug candidates failing during development.(p2) In recent years, the integration of computational algorithms, including artificial intelligence (AI) and ML, has significantly transformed the workflow of drug discovery by employing data-driven predictions and simulations.(p3),(p4),(p5) However, despite their growing success, classical computational protocols still struggle with certain fundamental challenges, especially when dealing with the quantum mechanical nature of molecular interactions and the high dimensionality of chemical space.(p6) This is where quantum computing (QC) and QML bring transformative opportunities. These technologies capitalize on the principles of quantum mechanics, such as superposition and entanglement, to process complex information beyond the capabilities of classical systems (Figure 1).(p7) Quantum computation can naturally simulate molecular behavior at the atomic level, making it ideal for modeling the advanced complexity of an interaction with higher precision.(p8) This opens new avenues for accurately predicting drug–target binding affinities, reaction mechanisms, and pharmacokinetic attributes. Furthermore, QML integrates QC with ML to enhance molecular-level predictions, clustering, and drug repurposing assignments.(p9) Hybrid quantum–classical algorithms are also being investigated to optimize molecular conformations and energy landscapes more efficiently. These algorithms leverage the strengths of both quantum and classical computing, enabling more accurate modeling of quantum phenomena at the molecular level. However, despite the growing interest in these innovations, several limitations hinder their widespread application in drug discovery.

In this review, we aim to provide a comprehensive analysis of the current state of quantum ML applications in drug discovery, with the explicit intent of bridging the gap between emerging quantum computational strategies and practical pharmaceutical research needs. Our objective is to briefly explain how QML can address specific bottlenecks in the traditional and AI-driven drug discovery pipelines, such as molecular property prediction, binding affinity estimation, docking simulations, and de novo drug design. The endpoint of this review is to present a clear, evidence-based perspective on where and how QML offers tangible advantages over conventional computational approaches, as well as to outline the remaining limitations and challenges that must be addressed before widespread adoption. By doing so, we aim to equip researchers, computational chemists, and pharmaceutical developers with a roadmap for integrating quantum intelligence into future drug discovery workflows, ensuring both scientific advancement and translational relevance.