Biomolecular Simulation

Biomolecular simulation aims to computationally model the behavior of biological molecules, enabling insights into processes inaccessible through experimentation. Current research heavily emphasizes the development and application of machine learning (ML) potentials, employing architectures like graph neural networks and equivariant neural networks, to improve both the accuracy and efficiency of simulations, particularly for large and complex systems. These advancements are significantly impacting drug discovery, protein engineering, and our fundamental understanding of biomolecular dynamics by enabling longer and more accurate simulations at a reduced computational cost. The integration of ML methods with established molecular mechanics force fields is also a key area of progress, leading to hybrid approaches that balance accuracy and speed.