Abstract

Computational biophysics has emerged as a critical field that applies advanced computational techniques to model and simulate biological systems at various scales, from molecular interactions to whole-cell processes. These approaches allow researchers to explore the complex dynamics of biological phenomena that are often difficult to study experimentally. This manuscript reviews several key computational methods used in biophysics, including molecular dynamics (MD) simulations, Monte Carlo (MC) simulations, quantum mechanical calculations, and coarse-grained models. We discuss how these techniques are applied to understand protein folding, molecular recognition, enzyme catalysis, and cellular signaling, among other biological processes. Additionally, we highlight the strengths, limitations, and recent advances in computational biophysics, and explore how these approaches are integrated with experimental data to provide a more comprehensive understanding of biological systems. The future of computational biophysics lies in the continued development of more accurate models, improved computational power, and the integration of machine learning and artificial intelligence.