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AutoDock-GPU: AutoDock for GPUs and other accelerators
<img src="logo.png" width="200">About
- AutoDock-GPU is developed by the Forli lab at Scripps Research.
- OpenCL and Cuda accelerated version of AutoDock4.2.6. It leverages its embarrasingly parallelizable LGA by processing ligand-receptor poses in parallel over multiple compute units.
- The OpenCL version was developed in collaboration with TU-Darmstadt and is able to target CPU, GPU, and FPGA architectures. This version itself was based on work done by Imre Pechan from evopro Innovation Kft.
- The Cuda version was developed in collaboration with Nvidia to run AutoDock-GPU on the Oak Ridge National Laboratory's (ORNL) Summit, and it included a batched ligand pipeline developed by Aaron Scheinberg from Jubilee Development.
- The SYCL version was developed in collaboration with Intel. This version can run CPU, GPU, and FPGA architectures.
Citation
Accelerating AutoDock4 with GPUs and Gradient-Based Local Search, J. Chem. Theory Comput. 2021, 10.1021/acs.jctc.0c01006
Features
- Gradient-based local search methods (e.g. ADADELTA), as well as an improved version of Solis-Wets from AutoDock 4.
- It targets platforms based on GPU as well as multicore CPU accelerators.
- Observed speedups of up to 4x (quad-core CPU) and 56x (GPU) over the original serial AutoDock 4.2 (Solis-Wets) on CPU. The Cuda version is currently even faster than the OpenCL version.
- A batched ligand pipeline to run virtual screenings on the same receptor (both OpenCL and Cuda)
Setup
Operating system | CPU | GPU |
---|---|---|
CentOS 6.7 & 6.8 / Ubuntu 14.04, 16.04, 20.04, 22.04 | Intel SDK for OpenCL 2017, oneAPI 2023.1 | AMD APP SDK v3.0 / CUDA 9, 10, and 11, 12 / oneAPI 2023.1 |
macOS Catalina 10.15.1 | Apple / Intel | Apple / Intel Iris, Radeon Vega 64, Radeon VII |
Other environments or configurations likely work as well, but are untested.
Compilation
The first step is to set environmental variables GPU_INCLUDE_PATH
and GPU_LIBRARY_PATH
,
as described here: https://github.com/ccsb-scripps/AutoDock-GPU/wiki/Guideline-for-users
For SYCL, setup the environment as described here: https://www.intel.com/content/www/us/en/docs/oneapi/programming-guide/2023-0/oneapi-development-environment-setup.html
make DEVICE=<TYPE> NUMWI=<NWI>
Parameters | Description | Values |
---|---|---|
<TYPE> | Accelerator chosen | CPU , GPU , CUDA , OCLGPU , XeGPU |
<NWI> | work-group/thread block size | 1 , 2 , 4 , 8 , 16 , 32 , 64 , 128 , 256 , 512 , 1024 |
When DEVICE=GPU
is chosen, the Makefile will automatically tests if it can compile Cuda succesfully. To override, use DEVICE=CUDA
or DEVICE=OCLGPU
or DEVICE=XeGPU
. The cpu target is only supported using OpenCL. Furthermore, an OpenMP-enabled overlapped pipeline (for setup and processing) can be compiled with OVERLAP=ON
.
Note that the version compiled with DEVICE=XeGPU
can also run on CPUs.
Hints: The best work-group size depends on the GPU and workload. Try NUMWI=128
or NUMWI=64
for modern cards with the example workloads. Not all work-group sizes work with all accelerators. On macOS, use NUMWI=1
for CPUs.
After successful compilation, the host binary autodock_<type>_<N>wi is placed under bin.
Binary-name portion | Description | Values |
---|---|---|
<type> | Accelerator chosen | cpu , gpu , xegpu |
<N> | work-group/thread block size | 1 , 2 , 4 , 8 ,16 , 32 , 64 , 128 , 256 |
Usage
Basic command
./bin/autodock_<type>_<N>wi \
--ffile <protein>.maps.fld \
--lfile <ligand>.pdbqt \
--nrun <nruns>
Mandatory options | Description | Value | |
---|---|---|---|
--ffile | -M | Protein file | <protein>.maps.fld |
--lfile | -L | Ligand file | <ligand>.pdbqt |
Both options can alternatively be provided in the contents of the files specified with --filelist (-B)
(see below for format) and --import_dpf (-I)
(AD4 dpf file format).
Example
./bin/autodock_gpu_64wi \
--ffile ./input/1stp/derived/1stp_protein.maps.fld \
--lfile ./input/1stp/derived/1stp_ligand.pdbqt
By default the output log file is written in the current working folder. Examples of output logs can be found under examples/output.
Supported arguments
Argument | Description | Default value <tr><td colspan="4">INPUT</td></tr> | |
---|---|---|---|
--lfile | -L | Ligand pdbqt file | no default |
--ffile | -M | Grid map files descriptor fld file | no default |
--flexres | -F | Flexible residue pdbqt file | no default |
--filelist | -B | Batch file | no default |
--import_dpf | -I | Import AD4-type dpf input file (only partial support) | no default |
--xraylfile | -R | reference ligand file for RMSD analysis | ligand file <tr><td colspan="4">CONVERSION</td></tr> |
--xml2dlg | -X | One (or many) AD-GPU xml file(s) to convert to dlg(s) | no default <tr><td colspan="4">OUTPUT</td></tr> |
--resnam | -N | Name for docking output log | ligand basename |
--contact_analysis | -C | Perform distance-based analysis (description below) | 0 (no) |
--xmloutput | -x | Specify if xml output format is wanted | 1 (yes) |
--dlgoutput | -d | Control if dlg output is created | 1 (yes) |
--dlg2stdout | -2 | Write dlg file output to stdout (if not OVERLAP=ON) | 0 (no) |
--rlige | Print reference ligand energies | 0 (no) | |
--gfpop | Output all poses from all populations of each LGA run | 0 (no) | |
--npdb | # pose pdbqt files from populations of each LGA run | 0 | |
--gbest | Output single best pose as pdbqt file | 0 (no) | |
--clustering | Output clustering analysis in dlg and/or xml file | 1 (yes) | |
--hsym | Handle symmetry in RMSD calc. | 1 (yes) | |
--rmstol | RMSD clustering tolerance | 2 (Å) <tr><td colspan="4">SETUP</td></tr> | |
--devnum | -D | OpenCL/Cuda device number (counting starts at 1) | 1 |
--loadxml | -c | Load initial population from xml results file | no default |
--seed | -s | Random number seeds (up to three comma-sep. integers) | time, process id <tr><td colspan="4">SEARCH</td></tr> |
--heuristics | -H | Ligand-based automatic search method and # evals | 1 (yes) |
--heurmax | -E | Asymptotic heuristics # evals limit (smooth limit) | 12000000 |
--autostop | -A | Automatic stopping criterion based on convergence | 1 (yes) |
--asfreq | -a | AutoStop testing frequency (in # of generations) | 5 |
--nrun | -n | # LGA runs | 20 |
--nev | -e | # Score evaluations (max.) per LGA run | 2500000 |
--ngen | -g | # Generations (max.) per LGA run | 42000 |
--lsmet | -l | Local-search method | ad (ADADELTA) |
--lsit | -i | # Local-search iterations (max.) | 300 |
--psize | -p | Population size | 150 |
--mrat | Mutation rate | 2 (%) | |
--crat | Crossover rate | 80 (%) | |
--lsrat | Local-search rate | 100 (%) | |
--trat | Tournament (selection) rate | 60 (%) | |
--dmov | Maximum LGA movement delta | 6 (Å) | |
--dang | Maximum LGA angle delta | 90 (°) | |
--rholb | Solis-Wets lower bound of rho parameter | 0.01 | |
--lsmov | Solis-Wets movement delta | 2 (Å) | |
--lsang | Solis-Wets angle delta | 75 (°) | |
--cslim | Solis-Wets cons. success/failure limit to adjust rho | 4 | |
--stopstd | AutoStop energy standard deviation tolerance | 0.15 (kcal/mol) | |
--initswgens | Initial # generations of Solis-Wets instead of -lsmet | 0 (no) <tr><td colspan="4">SCORING</td></tr> | |
--derivtype | -T | Derivative atom types (e.g. C1,C2,C3=C/S4=S/H5=HD) | no default |
--modpair | -P | Modify vdW pair params (e.g. C1:S4,1.60,1.200,13,7) | no default |
--ubmod | -u | Unbound model: 0 (bound), 1 (extended), 2 (compact) | 0 (same as bound) |
--smooth | Smoothing parameter for vdW interactions | 0.5 (Å) | |
--elecmindist | Min. electrostatic potential distance (w/ dpf: 0.5 Å) | 0.01 (Å) | |
--modqp | Use modified QASP from VirtualDrug or AD4 original | 0 (no, use AD4) |
Autostop is ON by default since v1.4. The collective distribution of scores among all LGA populations
is tested for convergence every <asfreq>
generations, and docking is stopped if the top-scored poses
exhibit a small variance. This avoids wasting computation after the best docking solutions have been found.
The heuristics set the number of evaluations at a generously large number. They are a function
of the number of rotatable bonds. It prevents unreasonably long dockings in cases where autostop fails
to detect convergence.
In our experience --heuristics 1
and --autostop 1
allow sufficient score evaluations for searching
the energy landscape accurately. For molecules with many rotatable bonds (e.g. about 15 or more)
it may be advisable to increase --heurmax
.
When the heuristics is used and --nev <max evals>
is provided as a command line argument it provides the (hard) upper # of evals limit to the value the heuristics suggests. Conversely, --heurmax
is the rolling-off type asymptotic limit to the heuristic's # of evals formula and should only be changed with caution.
The batch file is a text file containing the parameters to --ffile
, --lfile
, and --resnam
each on an individual line. It is possible to only use one line to specify the Protein grid map file which means it will be used for all ligands. Here is an example:
./receptor1.maps.fld
./ligand1.pdbqt
Ligand 1
./receptor2.maps.fld
./ligand2.pdbqt
Ligand 2
./receptor3.maps.fld
./ligand3.pdbqt
Ligand 3
When the distance-based analysis is used (--contact_analysis 1
or --contact_analysis <R_cutoff>,<H_cutoff>,<V_cutoff>
),
the ligand poses of a given run (either after a docking run or even when --xml2dlg <xml file(s)>
is used) are analyzed in
terms of their individual atom distances to the target protein with individual cutoffs for:
R
eactive (default: 2.1 Å): These are interactions between modified atom types numbered 1, 4, or 7 (i.e. between C1 and S4)H
ydrogen bonds (default: 3.7 Å): Interactions between Hydrogen-bond donor (closest N,O,S to an HD, or HD otherwise) and acceptor atom types (NA,NS,OA,OS,SA atom types).V
an der Waals (default: 4.0 Å): All other interactions not fulfilling the above criteria.
The contact analysis results for each pose are output in dlg lines starting with ANALYSIS:
and/or in <contact_analysis>
blocks in xml file output.
Documentation
Visit the project Wiki.
Contributing
- If you have a bug report, please check the open issues, and if it has not been reported yet, open a new one.
- If you want to add a new feature, pull/fork the code and submit a pull request.