Algorithmic Approaches in Molecular Modeling: A Computer Engineering Perspective

Molecular modeling is a crucial aspect of modern chemistry, enabling researchers to simulate and analyze molecular structures and interactions at an atomic level. This review paper explores various algorithmic approaches in molecular modeling, emphasizing the contributions of computer engineering to enhance computational efficiency and accuracy. We begin by discussing foundational algorithms, including molecular dynamics and Monte Carlo simulations, and their evolution over time. The integration of advanced techniques such as machine learning and artificial intelligence is highlighted, showcasing how these innovations facilitate predictive modeling and data-driven insights in chemical research. Furthermore, we examine the role of high-performance computing and parallel processing in accelerating complex simulations, enabling the exploration of larger systems and longer time scales. Challenges such as computational resource limitations and algorithm scalability are also addressed, alongside potential solutions derived from recent advancements in computer engineering. Ultimately, this review aims to bridge the gap between computer engineering and molecular modeling, providing a comprehensive overview of how algorithmic innovations are reshaping the landscape of computational chemistry.

کلیدواژه ها:

Molecular modeling ، Algorithmic Techniques ، Machine Learning ، High performance computing