Pharmaceutical Data Optimisation Using Quantum Machine Learning

Abstract

The pharmaceutical industry stands at the forefront of scientific innovation, aiming to develop novel drugs that address the world’s most pressing health challenges. However, the process of drug discovery and development is wrought withchallenges, including the need for precision in molecular modelling, the efficientselection of promising drug candidates, and the rigorous evaluation of safety profiles. In response to these challenges, this research project explores the fusion ofquantum computing and machine learning to revolutionize pharmaceutical dataanalysis. Quantum computing offers an unprecedented opportunity to simulatemolecular structures and properties with unparalleled accuracy. In tandem withquantum machine learning algorithms, this research harnesses the power of quantum computational supremacy to unlock hidden insights within pharmaceuticaldatasets. By leveraging the capabilities of quantum computing for molecular simulation and the data analysis prowess of quantum machine learning, this studyseeks to expedite drug discovery, optimize candidate selection, and enhance drugsafety assessments.