Molecular recognition is of fundamental importance in biology, facilitating the flows of matter, energy, and information through metabolic and signaling pathways. Concerned experimental and computational studies are now playing ever important roles in detailed mechanistic studies of molecular recognition. Among these studies, free energy simulations have established as a set of crucial computational tools. However, free energy simulations are expensive due to the extensive sampling required in nontrivial applications in large and complex biomolecules of biomedical importance. What further complicated the picture is the ubiquitous long-range electrostatic interactions that are harder to compute per step and requiring more steps to converge. An interesting direction to address these computational challenges is to explore alternative hardware, such as the graphics processing units (GPUs), which have gradually been employed in a range of scientific problems with impressive speedup. In this presentation, we will summarize our recent developments to facilitate free energy simulations in both explicit and implicit solvents on the GPUs and their applications in modeling ionic systems and protein-ligand binding interactions.

Free-energy simulations in explicit and implicit solvent models on GPUs

2019 Spring National Meeting

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