@article{89c1117716ec45f4bd22c850c6197847,
title = "Toward Fast and Accurate Binding Affinity Prediction with pmemdGTI: An Efficient Implementation of GPU-Accelerated Thermodynamic Integration",
abstract = "We report the implementation of the thermodynamic integration method on the pmemd module of the AMBER 16 package on GPUs (pmemdGTI). The pmemdGTI code typically delivers over 2 orders of magnitude of speed-up relative to a single CPU core for the calculation of ligand-protein binding affinities with no statistically significant numerical differences and thus provides a powerful new tool for drug discovery applications.",
author = "Lee, {Tai Sung} and Yuan Hu and Brad Sherborne and Zhuyan Guo and York, {Darrin M.}",
note = "Funding Information: This work was funded in part by Merck Research Laboratories, including financial support (to Y.H.) from the Postdoctoral Research Fellows Program, and the technical support from the High Performance Computing (HPC) group at Merck & Co. Inc. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by the National Science Foundation grant number OCI-1053575, with project number TG-MCB110101 (D.Y.). We gratefully acknowledge the support of the Nvidia Corporation with the donation of a GTX Titan X (Pascal) GPU and the GPU-time of a GPU-cluster where the reported benchmark results were performed. Publisher Copyright: {\textcopyright} 2017 American Chemical Society.",
year = "2017",
month = jul,
day = "11",
doi = "10.1021/acs.jctc.7b00102",
language = "English (US)",
volume = "13",
pages = "3077--3084",
journal = "Journal of Chemical Theory and Computation",
issn = "1549-9618",
publisher = "American Chemical Society",
number = "7",
}