Quantum Neural Networks for Accelerating Drug Discovery in Regenerative Medicine

Abstract

Regenerative medicine requires rapid analysis of the best therapeutic molecules capable of interacting with in vivo undifferentiated mass with complex biology and therapeutic properties that are needed to repair and regenerate tissues. The scalability and efficiency of conventional computational solutions and even recent representations with more advanced classical neural networks are limited when it comes to molecular data that are high dimensional. The combination of quantum computing principles and deep learning neural networks provides a potential avenue to speed this process up, by exploiting quantum parallelism, entanglement and superposition in pattern recognition and simulation of molecules through what is known as Quantum Neural Networks (QNNs). The paper describes how to apply QNNs to facilitate the process of target identification, molecular docking, and compound optimization in regenerative medicine in particular. We introduce a conceptual, hybrid quantum classical model to simulate protein-ligand interactions, screen drug candidates and economize on computational costs relative to classical models. The offered solution marks the conceivably quicker performance in molecular screening, better sensitivity in predicting drug-target interactions, and accommodating complex biological data. QNNs by connecting both quantum computing and biomedical innovation can foreseeably lead to much shorter time-to-discovery, yielding more successfully and personified regenerative treatments.