How Quantum Mechanics and Machine Learning Could Collaboratively Advance the Field of Pharmaceutical Research

Abstract

Drug discovery and development is a complex, lengthy, and expensive process, often taking over a decade and costing upwards of $2 billion to bring a new drug to market. There is a pressing need for innovative technologies that can accelerate and improve the efficiency of this process. Two emerging fields - quantum computing and machine learning - hold great promise in this regard. When combined, quantum machine learning has the potential to revolutionize pharmaceutical research by enabling rapid in silico drug screening, precision medicine, and drug discovery. This paper reviews the current pharmaceutical research and development pipeline, challenges therein, and how quantum machine learning can transform this pipeline. We discuss applications of quantum machine learning to target identification, molecular docking, molecular dynamics simulations, de novo drug design, clinical trials, and precision medicine. With exponential growth anticipated in quantum computing power and ever-advancing machine learning capabilities, quantum machine learning is poised to provide the next great leap forward in pharmaceutical sciences. This could significantly shorten development timelines, lower costs, and improve therapeutic success rates - providing immense social and economic benefits.