Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Quantum-Enhanced Approximation for Accelerating Protein Folding Dynamics in Molecular Biology Research

Sunita N. Dhote, Purushottam D. Shobhane, Priti C. Golar Assistant, Satyajit S Uparkar, Rajas Dhote

Published Nov 13, 2025• 0 citations
Publisher
DOI
10.1109/iceei68459.2025.11330367

Related papers

Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics

Soumen Pal, Manojit Bhattacharya, Sang‐Soo Lee +1 more

Molecular Biotechnology•May 27, 2023•84 citations

Quantum Bioinformatics and Protein Structure Prediction

Jincheng Zhang

This paper explores the potential of integrating quantum computing with bioinformatics to revolutionize protein structure prediction. Traditional methods face significant computational challenges, particularly with complex proteins. We propose a novel approach leveraging quantum algorithms to accelerate molecular simulations and integrate biological data for enhanced prediction accuracy. The core claim centers on the ability to achieve more precise protein structure determination through this combined methodology. Specifically, we outline a mechanism utilizing quantum algorithms to drastically reduce simulation times and subsequently, incorporate biological information for optimization. This represents a new paradigm in protein structure prediction, offering a pathway to accurately model protein folding and dynamics. The work delves into the theoretical foundations and potential algorithmic implementations, laying the groundwork for future research and development in this exciting field. The focus is on the computational speedup offered by quantum approaches and the strategic integration with existing bioinformatics techniques.

•Sep 6, 2026•0 citations

Quantum Computing Applications in Molecular Farming Research

Dr. Thomas Joseph Martinez, Liu Wenbo, Rebecca William Keller

Molecular farming represents a revolutionary approach to producing pharmaceuticals, vaccines, and industrial proteins using plants and plant cell cultures as biofactories. The integration of quantum computing into molecular farming research promises to address computational bottlenecks in protein engineering, metabolic pathway optimization, and crop improvement. This paper explores the emerging applications of quantum computing in molecular farming, examining how quantum algorithms can accelerate drug discovery, optimize protein folding predictions, and enhance synthetic biology approaches. We discuss the current state of quantum computing technologies, their specific applications in plant-based biomanufacturing, and the challenges that must be overcome for widespread implementation. The convergence of quantum computing and molecular farming has the potential to transform biopharmaceutical production, offering more efficient, cost-effective, and environmentally sustainable alternatives to traditional manufacturing methods.

Journal of Agriculture Digitalization and Research•Jan 1, 2025•0 citations

Quantum theory on protein folding

Liaofu Luo

Science China Physics Mechanics and Astronomy•Jan 17, 2014•21 citations

基于量子计算的蛋白质折叠预测

Jincheng Zhang

Protein folding prediction is a critical challenge in bioinformatics, demanding significant computational resources. This research explores the potential of quantum computing to accelerate and enhance this process. We formulate the protein folding problem as a quantum optimization problem, employing quantum simulation algorithms for efficient solution. The methodology involves mapping the protein's amino acid sequence into a quantum Hamiltonian, followed by simulating the system using quantum algorithms. We investigate the effectiveness of Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA) in determining the ground state energy and, consequently, the folded conformation of the protein. The predicted structures are then validated through comparison with known experimental data and integration with classical molecular dynamics simulations. The core claim of this work is to leverage the computational power of quantum computers to drastically reduce the time required for protein folding prediction, leading to improved accuracy and efficiency. The results demonstrate a promising approach for tackling this computationally intensive problem, potentially revolutionizing drug discovery and protein engineering.

•Sep 7, 2026•0 citations

Quantum computing solutions for protein folding

Don Roosan, Rubayat Islam Khan

Elsevier eBooks•Jan 1, 2026•0 citations
This paper explores the potential of integrating quantum computing with bioinformatics to revolutionize protein structure prediction. Traditional methods face significant computational challenges, particularly with complex proteins. We propose a novel approach leveraging quantum algorithms to accelerate molecular simulations and integrate biological data for enhanced prediction accuracy. The core claim centers on the ability to achieve more precise protein structure determination through this combined methodology. Specifically, we outline a mechanism utilizing quantum algorithms to drastically reduce simulation times and subsequently, incorporate biological information for optimization. This represents a new paradigm in protein structure prediction, offering a pathway to accurately model protein folding and dynamics. The work delves into the theoretical foundations and potential algorithmic implementations, laying the groundwork for future research and development in this exciting field. The focus is on the computational speedup offered by quantum approaches and the strategic integration with existing bioinformatics techniques.
Molecular farming represents a revolutionary approach to producing pharmaceuticals, vaccines, and industrial proteins using plants and plant cell cultures as biofactories. The integration of quantum computing into molecular farming research promises to address computational bottlenecks in protein engineering, metabolic pathway optimization, and crop improvement. This paper explores the emerging applications of quantum computing in molecular farming, examining how quantum algorithms can accelerate drug discovery, optimize protein folding predictions, and enhance synthetic biology approaches. We discuss the current state of quantum computing technologies, their specific applications in plant-based biomanufacturing, and the challenges that must be overcome for widespread implementation. The convergence of quantum computing and molecular farming has the potential to transform biopharmaceutical production, offering more efficient, cost-effective, and environmentally sustainable alternatives to traditional manufacturing methods.
Protein folding prediction is a critical challenge in bioinformatics, demanding significant computational resources. This research explores the potential of quantum computing to accelerate and enhance this process. We formulate the protein folding problem as a quantum optimization problem, employing quantum simulation algorithms for efficient solution. The methodology involves mapping the protein's amino acid sequence into a quantum Hamiltonian, followed by simulating the system using quantum algorithms. We investigate the effectiveness of Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA) in determining the ground state energy and, consequently, the folded conformation of the protein. The predicted structures are then validated through comparison with known experimental data and integration with classical molecular dynamics simulations. The core claim of this work is to leverage the computational power of quantum computers to drastically reduce the time required for protein folding prediction, leading to improved accuracy and efficiency. The results demonstrate a promising approach for tackling this computationally intensive problem, potentially revolutionizing drug discovery and protein engineering.