Protein Folding and Drug Discovery Using Quantum Computing’s Grover’s Algorithm
Abstract
In this paper, we present a novel approach to protein folding and drug discovery leveraging Grover’s algorithm. Our protein folding methodology focuses on accurately determining the phi and psi angles to achieve optimal protein configurations, ensuring the system attains a low energy state. In drug discovery, our algorithm accommodates variable primary structures, incorporating a critical examination step for comprehensive analysis. Detailed simulations demonstrate the efficacy of our approach in identifying potential drug candidates with high accuracy. Enhancements to Grover’s algorithm are realized through the integration of the Assembly-ToQuantumCompiler, optimizing quantum circuit designs for increased efficiency. Additionally, we introduce the Quantum Binary Neural Network (QBNN) to improve search precision and computational performance. By combining quantum algorithms with advanced compilers and neural networks, we aim to revolutionize computational biology and accelerate drug discovery. These algorithms improve as quantum computers improve.
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