Quantum Neural Network for Drug Synergy in Cancer
S Rajasree, L Ashwin, Nitish RG, Thirishaa SV · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2024
Our project aims to confront the persistent challenge of developing effective cancer treatments, especially in light of drug resistance. n the quest for improved therapeutic outcomes, clinicians often resort to combining drugs, yet the task of selecting the optimal mix remains daunting. Herein lies our innovative proposal - Quantum Neural Networks (QNNs). By harnessing the computational prowess inherent in QNNs, we aim to unravel the intricate complexities of genetic data, thereby pinpointing elusive yet promising drug interactions that traditional methodologies may overlook.Through this ambitious endeavor, our aim is to expedite the drug discovery process and to tailor treatments to the unique genetic profiles of individual patients. In forging this path, our project not only promises hope in the ongoing battle against cancer but also represents a monumental leap forward in the realm of personalized medicine Keywords: Cancer treatments, drug resistance, therapeutic outcomes, drug combination, optimal mix, Quantum Neural Networks (QNNs), computational prowess, genetic data, drug interactions, traditional methodologies, drug discovery process, personalized medicine, genetic profiles, individual patients, cancer battle, innovation in medicine Keywords: Cancer , Quantum Neural Networks , Drug Synergy , Sensitivity , Specificity , Synergy Score