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Quantum-Enhanced Drug Discovery Using HybridAI and Quantum Computing for Molecular Property Prediction and Virtual Screening

Sujithkumar K., Abirami K.

International Conference on Interdisciplinary Research in Science, Engineering, and TechnologyJan 1, 20250 citations

Quantum Kernel Learning for Drug Discovery: A Hybrid QSVR and Quantum Kernel-Based Random Forest Approach for Molecular Screening

Rakshath Raj Y, Chelvachezhiyan S N, Praveen S +2 more

IEEE Region 10 Symposium (TENSYMP)Jun 1, 20260 citations

Acceleration of Variational Quantum Eigen Solver

Manish Modani

Lecture notes in networks and systemsJan 1, 20240 citations

Drug Discovery Using Variational Quantum EigenSolver

S. Surya Prakash, P. Banu Priya, Sudarshan Someneni +2 more

Lecture notes in networks and systemsJan 1, 20252 citations

Hybrid Quantum-Classical Algorithms for Drug Discovery and Molecular Simulation

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 integrationAbstract: 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

International Journal of Academic and Industrial Research Innovations(IJAIRI)Jun 27, 20251 citation

Variational Quantum-Classical Optimization for Molecular Feature Generation: A Hybrid Machine Learning Approach

Abiodun Emmanuel Sojobi

Dec 17, 20250 citations

Variational Quantum Eigen Solver Based Molecular Toxicity Prediction for Drug Candidate Screening

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