Abstract
In recent years, substantial modelling breakthroughs have been achieved in artificial intelligence due to new algorithms, improved computer power, and expanded storage capacity. These factors have made it possible to process large amounts of data in a short amount of time. By using quantum computing in conjunction with deep learning models, it has been possible to explain the characteristics of ligands and their interactions with biological targets. This contributes to the process of ligand identification and ultimately results in the optimization of drug design. This review explains the extensive use of quantum deep learning in the development of drug design from traditional to quantum-powered deep learning neural networks that cover some domains like variational quantum Eigen solver, variational quantum circuits, quantum convolutional deep neural networks, QC-based deep neural networks for QSAR, as well as quantized generative models for the discovery of small drug molecules. Quantum computing can execute incredible computational work tenfold faster than current technology, transforming drug design, development, and post-marketing surveillance. This will reduce the time and resources needed to develop a medicine. Scientific research is moving toward quantum computing since it is anticipated that QC-based deep learning technologies can predict and mimic the characteristics, structures, and activities of molecules more efficiently than different ML techniques or conventional computers.
