Drug discovery is a multifaceted process. This procedure typically includes various stages,
such as fundamental research, preclinical research, clinical research, and FDA approval …

Quantum computing, a transformative field that emerged from quantum mechanics and
computer science, has gained immense attention for its potential to revolutionize …

The primary objective of this paper is to conduct a comparative analysis between two
Machine Learning approaches: Tensor Networks (TN) and Variational Quantum Classifiers …

Drug repositioning is a less expensive and time-consuming method than the traditional
method of drug discovery. It is a strategy for identifying new uses for approved or …

In the field of pharmaceutical science, there is an increasing demand for advanced
computational techniques to enhance drug discovery and understand complex molecular …

Quantum algorithms which were derived from quantum mechanics principles are further
revolutionizing this process with their potential for detailed simulation of molecular …

Effective therapy for Alzheimer's disease depends on early diagnosis. Key results from four
research that used machine learning to detect Alzheimer's disease during its initial level …

The QNN (quantum neural network) algorithmic approach is to interpreted the forward
networking in the dataset. Alike, some classical dataset structure take input from the layer of …

Abstract Design and development of new drug molecules are essential for the survival of
human society. New drugs are designed for therapeutic purposes to combat new diseases …

Using quantum algorithms to improve deep learning models' capabilities is becoming
increasingly popular as quantum computing develops. In this work, we investigate how …