"A Hybrid Quantum Tao–Shor Algorithm for High-Assurance Molecular Analysis in Drug Discovery" (IEEE)

Yoon A Limsuwan

Yoon A Limsuwan

Yoon A Limsuwan

Murali Krishna Pasupuleti

Abstract: This study explores the implementation of hybrid quantum-classical algorithms in the realm of drug discovery and molecular simulation, with an emphasis on optimizing molecular interaction prediction. The integration of quantum computing paradigms, particularly the Variational Quantum Eigensolver (VQE), with classical machine learning techniques is evaluated for its efficacy in simulating molecular systems. Key results demonstrate superior accuracy in predicting binding affinities and reaction energetics using the hybrid models compared to traditional methods. The findings offer significant implications for accelerating the drug development pipeline, particularly in early-stage candidate screening. Keywords: Hybrid computing, Quantum simulation, Molecular interaction, Drug discovery, Variational Quantum Eigensolver, Quantum-classical integration

Yoon A Limsuwan

B S Anupama, Amrit S R, A Chetan Kumar

The large search space and the challenge of accurately simulating quantum-level molecular interactions are the fundamental causes of the delayed, complicated, and costly process of discovering new medications. The effectiveness of conventional computational techniques, including molecular docking and molecular dynamics, to assess molecular characteristics and provide highly accurate drug candidate predictions is constrained. This paper presents a Quantum-Assisted Drug Discovery Simulation framework that combines cutting-edge quantum computing algorithms, specifically the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE), with traditional molecular docking tools like RDKit and AutoDock. The system uses these quantum algorithms to perform very accurate Quantum Refinement on the most potential therapeutic protein poses found by classical identification. This hybrid approach seeks to optimize drug-protein interactions more quickly, speed up early-stage drug discovery, and create opportunities for less costly, more accurate medical research, even though a combination of quantum and classical calculations allows more accurate prediction of molecular energies and binding affinities.