Computational biophysics has revolutionized drug discovery and biomolecular interaction studies by integrating theoretical models with experimental biophysical techniques. This chapter explores the synergy between computational approaches—such as molecular docking, molecular dynamics (MD) simulations, and free energy calculations—employ biophysical principles to investigate molecular interactions while simultaneously complementing techniques like X-ray crystallography, NMR spectroscopy, Cryo-EM, surface plasmon resonance (SPR), and isothermal titration calorimetry (ITC). These approaches provide atomic-level insights into molecular structures, binding affinities, and conformational dynamics, improving the efficiency of structure-based drug design (SBDD) and protein-ligand interaction studies. Furthermore, AI-driven methodologies, including machine learning-assisted docking and MD simulations, have enhanced predictive accuracy, accelerating drug screening and lead optimization. Case studies highlight real-world applications, demonstrating how computational strategies validated by experimental data contribute to drug repurposing, enzyme engineering, and biomolecular interaction research. The integration of computational and biophysical techniques continues to push the boundaries of molecular medicine, offering new avenues for precision drug discovery and therapeutic innovation.

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Computational Approaches Employing Biophysical Principles for Drug Discovery and Biomolecular Interactions

  • Nikhil Pathak,
  • Vidhya Tangeda,
  • Jhanvi Rachh,
  • Budheswar Dehury

摘要

Computational biophysics has revolutionized drug discovery and biomolecular interaction studies by integrating theoretical models with experimental biophysical techniques. This chapter explores the synergy between computational approaches—such as molecular docking, molecular dynamics (MD) simulations, and free energy calculations—employ biophysical principles to investigate molecular interactions while simultaneously complementing techniques like X-ray crystallography, NMR spectroscopy, Cryo-EM, surface plasmon resonance (SPR), and isothermal titration calorimetry (ITC). These approaches provide atomic-level insights into molecular structures, binding affinities, and conformational dynamics, improving the efficiency of structure-based drug design (SBDD) and protein-ligand interaction studies. Furthermore, AI-driven methodologies, including machine learning-assisted docking and MD simulations, have enhanced predictive accuracy, accelerating drug screening and lead optimization. Case studies highlight real-world applications, demonstrating how computational strategies validated by experimental data contribute to drug repurposing, enzyme engineering, and biomolecular interaction research. The integration of computational and biophysical techniques continues to push the boundaries of molecular medicine, offering new avenues for precision drug discovery and therapeutic innovation.