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Welcome to the AutoDock-GPU wiki!

AutoDock-GPU is an OpenCL implementation of AutoDock LGA.

Main project is hosted under: https://autodock.scripps.edu

Features

AutoDock-GPU leverages the embarrassingly parallelism of AutoDock LGA, by processing ligand-receptor poses in parallel over multiple compute units on a GPU device.

Besides the legacy Solis-Wets local search method, AutoDock-GPU adds newly implemented local-search methods based on gradients of the scoring function.

One of these methods, ADADELTA, has proven to increase significantly the docking quality in terms of scores and RMSDs.

License

AutoDock-GPU is released under the GNU GPL v2 and GNU LGPL v2.1 licenses. See details.

Guidelines

• Users
• Developers

Citation

A paper describing the OpenCL implementation and evaluation of AutoDock-GPU is available on the Journal of Chemical Theory and Computation

If you find AutoDock-GPU useful for your work, please cite it as:

@article{autodockgpu,
  title = {Accelerating AutoDock4 with GPUs and Gradient-Based Local Search},
  author = {Santos-Martins, Diogo and Solis-Vasquez, Leonardo and Tillack, Andreas F and Sanner, Michel F and Koch, Andreas and Forli, Stefano},
  journal = {Journal of Chemical Theory and Computation},
  volume = {17},
  number = {2},
  pages = {1060-1073},
  year = {2021},
  doi = {10.1021/acs.jctc.0c01006},
  publisher = {ACS Publications}
}

The preprint preceeding this publication is available on ChemRxiv using an earlier version (v1.1) of AutoDock-GPU.

A full list of publications is available here.

Support

If you encounter any issues or have any questions, please use any of the following:

• The GitHub issues page: https://github.com/ccsb-scripps/AutoDock-GPU/issues

You can also contact the main developers via email:

• Diogo Santos-Martins: diogom@scripps.edu
• Leonardo Solis-Vasquez: solis@esa.tu-darmstadt.de
• Andreas Tillack: atillack@scripps.edu