Chiral recognition is an important issue in the drug discovery, food additive, agrochemical and fragrance industries. Employing chiral stationary phases (CSPs) in high performance liquid chromatography (HPLC) is an important way to achieve chiral resolution. It is important to investigate the recognition mechanisms of chiral selectors interacting with chiral analytes, and molecular dynamics (MD) simulations provide an atomic-scale perspective of such recognition processes. The present study will focus on implementing GPU-accelerated MD simulations to explore the pH-dependent chiral recognition process of a monoproline selector.<br/><br/>General-purpose computation on graphics processing units (GPGPU) technology has demonstrated superior speed and efficiency in high performance computation compared to the traditional computing power of central processing units (CPUs). Due to increased size and complexity of current chiral interfaces, and longer term simulation goals, our traditional CPU simulation code is no longer able to meet our computing needs. Consequently, the present study adopted Nvidia CUDA GPU programming model to construct a GPU-accelerated implementation from our customized MD simulation program. CUDA/C++ APIs and derivatives were applied to the Lennard-Jones forces and the Ewald summation for electrostatic interactions in the GPU-accelerated MD simulation program. The benchmarks from the GPU-accelerated simulations realized a significant performance increase compared to CPU implementations. Along with benchmarking and code performance information, simulation results from the monoproline interfaces under various pH levels will be presented, including an examination of the resolution mechanism.

Implementation of GPU-accelerated molecular dynamics simulations of monoproline chiral interfaces

255th National Meeting (2018)

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