Revolutionizing Biological Network Modeling: Quantum Computing Applications in Metabolic Pathways, Genetic Mutations, and Molecular Interactions

Biological networks, such as complex metabolic and cellular signaling pathways, are fundamental for understanding molecular interactions and advancing fields like personalized medicine, biotechnology, and drug discovery. However, the dynamic and nonlinear nature of these networks presents significant challenges for conventional modeling techniques. Quantum computing, leveraging principles like superposition and entanglement, offers a transformative approach to overcoming these limitations. By employing quantum algorithms such as Hamiltonian simulation and Grover's search, quantum tools facilitate precise modeling of molecular interactions, metabolic pathways, and genetic mutation effects. This study explores quantum platforms, including Qiskit and D-Wave Ocean SDK, alongside a comprehensive review of research spanning ۱۹۹۵ to ۲۰۲۰ and data from the Protein Data Bank (PDB). Applications investigated encompass mutation impact prediction, protein-ligand interaction studies, and optimization of metabolic pathways. These quantum-based methods hold promise for deeper insights into cellular processes, accelerated drug development, and enhanced efficiency of biological systems. Despite challenges such as algorithmic complexity, computational demands, and hardware limitations, advances in hybrid quantum-classical frameworks and emerging technologies offer a path forward. With its capacity for high-precision simulations and groundbreaking insights, quantum computing is poised to redefine the fields of biology and biochemistry, paving the way for novel scientific breakthroughs.

کلیدواژه ها:

Quantum computing ، Quantum methods ، Biology networks