AutoDock-GPU

The AutoDock-GPU Suite is a growing collection of methods for computational\ndocking and virtual screening, for use in structure-based drug discovery\nand exploration of the basic mechanisms of biomolecular structure and function.\nMore info on AutoDock-GPU be located at https://ccsb.scripps.edu/autodock/ and official github page

See here for a document describing prerequisites and setup steps\nfor all HPC containers and instructions for pulling NGC containers.

System requirements

Before running the NGC AutoDock-GPU container please ensure your system meets the following requirements.

One of the following container runtimes\n
- nvidia-docker
- Singularity >= 3.1
\n

x86_64

Pascal(sm60), Volta(sm70), or Ampere (sm80) NVIDIA GPU(s)
CUDA driver version >= r450, -or- r418, -or- r440

arm64

Pascal(sm60), Volta(sm70), or Ampere (sm80) NVIDIA GPU(s)
CUDA driver version >= r450

Running AutoDock-GPU

Executables

autodock_gpu_128wi: primary autodock executable

Command invocation

An example command is:

autodock_gpu_128wi -ffile protein.maps.fld -lfile rand-example.pdbqt -nrun 100 -lsmet ad -resnam CUDAout\n

Examples

The following examples demonstrate how to run the NGC AutoDock-GPU container under the supported runtimes.

docker\n
- command line execution
- interactive shell
\n
Singularity\n
- command line execution
- interactive shell
\n

Running with docker

Once authenticated the following modes of running are supported:

Command line execution with docker

In order to run the water benchmark: download and permission, the dataset from the AD-GPU_set_of_42.\nCopy and paste the following script, called check.py into your working directory.

#!/usr/bin/env python3\nimport csv\nimport glob\nimport os\nimport re\nimport subprocess\nimport sys\n\n\ndef run_all(executable):\n  dirlist = glob.glob(\"data/*\")\n  for d in dirlist:\n      os.chdir(d)\n      # There's potentially 3 runs to do here, doing 1 to save time.\n      for i in range(1):\n          subprocess.run(\"{} -ffile protein.maps.fld -lfile rand-{}.pdbqt -nrun 100 -lsmet ad -heuristics 1 -autostop 1 -maxnev 8000000 -resnam CUDAout-{}\".format(executable, i, i), shell=True).check_returncode()\n      os.chdir(\"../..\")\n\n\ndef process_file(filename):\n    re_ligandname = re.compile(r'^Ligand name: (.+)')\n    re_intermolecular = re.compile(r'^(?=.*\\bIntermolecular\\b).*=(.*)')\n    re_internal = re.compile(r'^(?=.*\\bInternal\\b).*=(.*)')\n    ligand = ''\n    curr_energy = sys.float_info.max\n    best_energy = sys.float_info.max\n    with open(filename) as f:\n        for line in f:\n            match = re_ligandname.search(line)\n            if match:\n                ligand = match.group(0).split()[2]\n                continue\n            match = re_intermolecular.search(line)\n            if match:\n                curr_energy = float(match.group(1).split()[0])\n                continue\n            match = re_internal.search(line)\n            if match:\n                curr_energy = curr_energy + float(match.group(1).split()[0])\n                if (curr_energy &lt; best_energy):\n                    best_energy = curr_energy\n                continue\n    return best_energy\n\n\ndef check_output(reference_energies):\n    results = {}\n    err = 0.0\n    cnt = 0\n    for key, val in reference_energies.items():\n        for dlgfile in glob.glob('data/*/CUDAout-*.dlg'):\n           best_energy = process_file(dlgfile)\n           diff = val - best_energy\n           err = err + diff\n           cnt = cnt + 1\n           try:\n               results[key].append(diff)\n           except KeyError:\n               results[key] = [diff]\n    return (err/cnt)\n\n\nreference_energies = {}\nwith open('ligand_properties.csv') as f:\n    reader = csv.reader(f)\n    cnt = 0\n    for row in reader:\n        cnt = cnt + 1\n        if cnt == 1:\n            continue\n        # There are some bad ligands for which we have no reference energy\n        if row[4] == '':\n            continue\n        reference_energies[row[1]] = float(row[4])\n\nfor numwi in [128]:\n    run_all(\"autodock_gpu_{}wi\".format(numwi))\n    meandiff = check_output(reference_energies)\n    print('{} : Mean Error: {:.3f}'.format(numwi, meandiff))\n    if meandiff &gt; 0.5:\n        exit(1)\n\nprint(\"Checks passed.\")\n\n

$ git clone https://github.com/diogomart/AD-GPU_set_of_42.git\n$ gunzip AD-GPU_set_of_42/data/*/*map.gz\n$ docker run -ti --gpus all -v :workdir --workdir /workdir nvcr.io/hpc/autodock:2020.06 sh -c \"./check.py\"\n

Example of successful AutoDock-GPU output:

AutoDock-GPU version: 09773678fc7e39677061d765b767f4bae8930fb7-dirty\nCUDA Setup time 2.031275s\n(Thread 0 is setting up Job 0)\nRunning Job #0:\nUsing heuristics: -lsmet sw -nev 7468504\nLocal-search chosen method is: swSolis-Wets (sw)\nRest of Setup time 0.037800s\nExecuting docking runs, stopping automatically after either reaching 0.15 kcal/mol standard deviation of\nthe best molecules of the last 4 * 5 generations, 27000 generations, or 7468504 evaluations:\nGenerations |  Evaluations |     Threshold    |  Average energy of best 10%  | Samples |    Best energy\n------------+--------------+------------------+------------------------------+---------+-------------------\n          0 |          150 |   -2.91 kcal/mol |   -4.10 +/-    0.40 kcal/mol |       5 |   -4.82 kcal/mol\n          5 |       125155 |   -2.91 kcal/mol |   -5.70 +/-    1.40 kcal/mol |    8066 |  -11.91 kcal/mol\n         10 |       250790 |   -5.69 kcal/mol |   -9.55 +/-    0.83 kcal/mol |     490 |  -12.35 kcal/mol\n         15 |       377698 |   -9.53 kcal/mol |  -11.80 +/-    0.42 kcal/mol |      52 |  -12.62 kcal/mol\n         20 |       505556 |  -11.68 kcal/mol |  -12.42 +/-    0.18 kcal/mol |      14 |  -12.67 kcal/mol\n         25 |       633659 |  -12.23 kcal/mol |  -12.48 +/-    0.19 kcal/mol |      16 |  -12.74 kcal/mol\n         30 |       762773 |  -12.30 kcal/mol |  -12.59 +/-    0.14 kcal/mol |      16 |  -12.83 kcal/mol\n         35 |       892173 |  -12.46 kcal/mol |  -12.73 +/-    0.15 kcal/mol |      14 |  -13.02 kcal/mol\n         40 |      1021489 |  -12.57 kcal/mol |  -12.81 +/-    0.13 kcal/mol |      14 |  -13.05 kcal/mol\n         45 |      1151059 |  -12.68 kcal/mol |  -12.83 +/-    0.11 kcal/mol |      19 |  -13.06 kcal/mol\n         50 |      1280984 |  -12.74 kcal/mol |  -12.84 +/-    0.10 kcal/mol |      13 |  -13.07 kcal/mol\nDocking time 3.916151s\n------------+--------------+------------------+------------------------------+---------+-------------------\n                                   Finished evaluation after reaching\n                                  -12.80 +/-    0.13 kcal/mol combined.\n                                60 samples, best energy   -13.07 kcal/mol.\nShutdown time 0.000570s\nJob #0 took 3.955 sec after waiting 3.373 sec for setup\n(Thread 0 is processing Job 0)\nRun time of entire job set (1 files): 8.413 sec\nSavings from multithreading: -2.039 sec\nIdle time of execution thread: 4.458 sec\nAll jobs ran without errors.\n

Interactive shell with docker

The following command will launch an interactive shell in the AutoDock-GPU\ncontainer using docker mounting $HOME/data from the underlying\nsystem as /data in the container:

$ docker run -it --rm --gpus all -v $HOME/data:/data --workdir /data nvcr.io/hpc/autodock:2020.06\n

Where:

-it: start the container with an interactive terminal (short for --interactive --tty)
--rm: make container ephemeral (removes container on exit)
-v $(pwd):/host_pwd: bind mount the current working directory into the container as /host_pwd
--gpus all: vanilla docker GPU provisioning
--workdir /data: sets working directory inside the container

This should produce a root prompt within the container:

root@3a8c8b7c3a88:/data#\n

Running with Singularity

Pull the image

Save the NGC AutoDock-GPU container as a local Singularity image file:

$ singularity build autodock.simg docker://nvcr.io/hpc/autodock:2020.06\n

The AutoDock-GPU Singularity image is now saved in the current directory as autodock.simg

Note: Singularity/2.x

In order to pull NGC images with singularity version 2.x and earlier, NGC container registry authentication credentials are required.

To set your NGC container registry authentication credentials:

$ export SINGULARITY_DOCKER_USERNAME='$oauthtoken'\n$ export SINGULARITY_DOCKER_PASSWORD=\n

More information describing how to obtain and use your NVIDIA NGC Cloud Services API key can be found\nhere.

Note: Singularity 3.1.x - 3.2.x

There is currently a bug in Singularity 3.1.x and 3.2.x causing the LD_LIBRARY_PATH to be incorrectly set within the container environment.\nAs a workaround The LD_LIBRARY_PATH must be unset before invoking Singularity:

$ LD_LIBRARY_PATH=\"\" singularity exec ...\n

Command line execution

In order to run a test benchmark: download, permission, and run the example script from the NGC Examples Repository.

wget ...\nchmod +x check.py\nsingularity run --nv -B ${PWD}:/host_pwd --pwd /host_pwd ./check.py\n

Example of successful AutoDock-GPU output:

AutoDock-GPU version: 09773678fc7e39677061d765b767f4bae8930fb7-dirty\nCUDA Setup time 2.031275s\n(Thread 0 is setting up Job 0)\nWarning: unknown argument '-maxnev'.\nRunning Job #0:\nUsing heuristics: -lsmet sw -nev 7468504\nLocal-search chosen method is: swSolis-Wets (sw)\nRest of Setup time 0.037800s\nExecuting docking runs, stopping automatically after either reaching 0.15 kcal/mol standard deviation of\nthe best molecules of the last 4 * 5 generations, 27000 generations, or 7468504 evaluations:\nGenerations |  Evaluations |     Threshold    |  Average energy of best 10%  | Samples |    Best energy\n------------+--------------+------------------+------------------------------+---------+-------------------\n          0 |          150 |   -2.91 kcal/mol |   -4.10 +/-    0.40 kcal/mol |       5 |   -4.82 kcal/mol\n          5 |       125155 |   -2.91 kcal/mol |   -5.70 +/-    1.40 kcal/mol |    8066 |  -11.91 kcal/mol\n         10 |       250790 |   -5.69 kcal/mol |   -9.55 +/-    0.83 kcal/mol |     490 |  -12.35 kcal/mol\n         15 |       377698 |   -9.53 kcal/mol |  -11.80 +/-    0.42 kcal/mol |      52 |  -12.62 kcal/mol\n         20 |       505556 |  -11.68 kcal/mol |  -12.42 +/-    0.18 kcal/mol |      14 |  -12.67 kcal/mol\n         25 |       633659 |  -12.23 kcal/mol |  -12.48 +/-    0.19 kcal/mol |      16 |  -12.74 kcal/mol\n         30 |       762773 |  -12.30 kcal/mol |  -12.59 +/-    0.14 kcal/mol |      16 |  -12.83 kcal/mol\n         35 |       892173 |  -12.46 kcal/mol |  -12.73 +/-    0.15 kcal/mol |      14 |  -13.02 kcal/mol\n         40 |      1021489 |  -12.57 kcal/mol |  -12.81 +/-    0.13 kcal/mol |      14 |  -13.05 kcal/mol\n         45 |      1151059 |  -12.68 kcal/mol |  -12.83 +/-    0.11 kcal/mol |      19 |  -13.06 kcal/mol\n         50 |      1280984 |  -12.74 kcal/mol |  -12.84 +/-    0.10 kcal/mol |      13 |  -13.07 kcal/mol\nDocking time 3.916151s\n------------+--------------+------------------+------------------------------+---------+-------------------\n                                   Finished evaluation after reaching\n                                  -12.80 +/-    0.13 kcal/mol combined.\n                                60 samples, best energy   -13.07 kcal/mol.\nShutdown time 0.000570s\nJob #0 took 3.955 sec after waiting 3.373 sec for setup\n(Thread 0 is processing Job 0)\nRun time of entire job set (1 files): 8.413 sec\nSavings from multithreading: -2.039 sec\nIdle time of execution thread: 4.458 sec\nAll jobs ran without errors.\n

Interactive shell

The following command will launch an interactive shell in the AutoDock-GPU\ncontainer using singularity shell:

$ singularity shell --nv -B \n

Where:

--nv: expose the host GPU(s) to the container
-B: bind user-defined directory into the container

This should produce a Singularity shell prompt within the container:

Singularity: Invoking an interactive shell within container...\n\nSingularity Autodock.simg:~/example&gt;\n

Suggested Reading

Autodock main page\nAutodock wiki\nAutodock officional github page

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(2020.06)","hpc/autodock","hpc/containers/autodock","autodock","The AutoDock Suite is a growing collection of methods for computational docking and virtual screening, for use in structure-based drug discovery and exploration of the basic mechanisms of biomolecular structure and function.","# AutoDock-GPU\nThe AutoDock-GPU Suite is a growing collection of methods for computational \ndocking and virtual screening, for use in structure-based drug discovery \nand exploration of the basic mechanisms of biomolecular structure and function.\nMore info on AutoDock-GPU be located at https://ccsb.scripps.edu/autodock/ and official [github page](https://github.com/ccsb-scripps/AutoDock-GPU)\n\n[See here](http://docs.nvidia.com/ngc/ngc-user-guide/index.html) for a document describing prerequisites and setup steps\nfor all HPC containers and instructions for pulling NGC containers.\n\n# System requirements\nBefore running the NGC AutoDock-GPU container please ensure your system meets the following requirements.\n\n* One of the following container runtimes\n * [nvidia-docker](https://github.com/NVIDIA/nvidia-docker)\n * [Singularity](https://www.sylabs.io/docs/) >= 3.1\n\n## x86_64\n* Pascal(sm60), Volta(sm70), or Ampere (sm80) NVIDIA GPU(s)\n* CUDA driver version >= r450, -or- r418, -or- r440\n\n## arm64\n* Pascal(sm60), Volta(sm70), or Ampere (sm80) NVIDIA GPU(s)\n* CUDA driver version >= r450\n\n# Running AutoDock-GPU\n\n#### Executables\n`autodock_gpu_128wi`: primary autodock executable\n\n#### Command invocation\nAn example command is:\n\n```bash\nautodock_gpu_128wi -ffile protein.maps.fld -lfile rand-example.pdbqt -nrun 100 -lsmet ad -resnam CUDAout\n```\n\n#### Examples\nThe following examples demonstrate how to run the NGC AutoDock-GPU container under the supported runtimes.\n* [docker](#running-with-docker)\n * [command line execution](#command-line-execution-with-docker)\n * [interactive shell](#interactive-shell-with-docker)\n* [Singularity](#running-with-singularity)\n * [command line execution](#command-line-execution-with-singularity)\n * [interactive shell](#interactive-shell-with-singularity)\n\n# Running with docker\n\nOnce authenticated the following modes of running are supported:\n## Command line execution with docker\n\n\nIn order to run the water benchmark: download and permission, the dataset from the [AD-GPU_set_of_42](https://github.com/diogomart/AD-GPU_set_of_42.git).\nCopy and paste the following script, called `check.py` into your working directory.\n\n```python\n#!/usr/bin/env python3\nimport csv\nimport glob\nimport os\nimport re\nimport subprocess\nimport sys\n\n\ndef run_all(executable):\n dirlist = glob.glob(\"data/*\")\n for d in dirlist:\n os.chdir(d)\n # There's potentially 3 runs to do here, doing 1 to save time.\n for i in range(1):\n subprocess.run(\"{} -ffile protein.maps.fld -lfile rand-{}.pdbqt -nrun 100 -lsmet ad -heuristics 1 -autostop 1 -maxnev 8000000 -resnam CUDAout-{}\".format(executable, i, i), shell=True).check_returncode()\n os.chdir(\"../..\")\n\n\ndef process_file(filename):\n re_ligandname = re.compile(r'^Ligand name: (.+)')\n re_intermolecular = re.compile(r'^(?=.*\\bIntermolecular\\b).*=(.*)')\n re_internal = re.compile(r'^(?=.*\\bInternal\\b).*=(.*)')\n ligand = ''\n curr_energy = sys.float_info.max\n best_energy = sys.float_info.max\n with open(filename) as f:\n for line in f:\n match = re_ligandname.search(line)\n if match:\n ligand = match.group(0).split()[2]\n continue\n match = re_intermolecular.search(line)\n if match:\n curr_energy = float(match.group(1).split()[0])\n continue\n match = re_internal.search(line)\n if match:\n curr_energy = curr_energy + float(match.group(1).split()[0])\n if (curr_energy < best_energy):\n best_energy = curr_energy\n continue\n return best_energy\n\n\ndef check_output(reference_energies):\n results = {}\n err = 0.0\n cnt = 0\n for key, val in reference_energies.items():\n for dlgfile in glob.glob('data/*/CUDAout-*.dlg'):\n best_energy = process_file(dlgfile)\n diff = val - best_energy\n err = err + diff\n cnt = cnt + 1\n try:\n results[key].append(diff)\n except KeyError:\n results[key] = [diff]\n return (err/cnt)\n\n\nreference_energies = {}\nwith open('ligand_properties.csv') as f:\n reader = csv.reader(f)\n cnt = 0\n for row in reader:\n cnt = cnt + 1\n if cnt == 1:\n continue\n # There are some bad ligands for which we have no reference energy\n if row[4] == '':\n continue\n reference_energies[row[1]] = float(row[4])\n\nfor numwi in [128]:\n run_all(\"autodock_gpu_{}wi\".format(numwi))\n meandiff = check_output(reference_energies)\n print('{} : Mean Error: {:.3f}'.format(numwi, meandiff))\n if meandiff > 0.5:\n exit(1)\n\nprint(\"Checks passed.\")\n\n```\n\n```bash\n$ git clone https://github.com/diogomart/AD-GPU_set_of_42.git\n$ gunzip AD-GPU_set_of_42/data/*/*map.gz\n$ docker run -ti --gpus all -v :workdir --workdir /workdir nvcr.io/hpc/autodock:2020.06 sh -c \"./check.py\"\n```\n\nExample of successful AutoDock-GPU output:\n\n``` bash\nAutoDock-GPU version: 09773678fc7e39677061d765b767f4bae8930fb7-dirty\nCUDA Setup time 2.031275s\n(Thread 0 is setting up Job 0)\nRunning Job #0:\nUsing heuristics: -lsmet sw -nev 7468504\nLocal-search chosen method is: swSolis-Wets (sw)\nRest of Setup time 0.037800s\nExecuting docking runs, stopping automatically after either reaching 0.15 kcal/mol standard deviation of\nthe best molecules of the last 4 * 5 generations, 27000 generations, or 7468504 evaluations:\nGenerations | Evaluations | Threshold | Average energy of best 10% | Samples | Best energy\n------------+--------------+------------------+------------------------------+---------+-------------------\n 0 | 150 | -2.91 kcal/mol | -4.10 +/- 0.40 kcal/mol | 5 | -4.82 kcal/mol\n 5 | 125155 | -2.91 kcal/mol | -5.70 +/- 1.40 kcal/mol | 8066 | -11.91 kcal/mol\n 10 | 250790 | -5.69 kcal/mol | -9.55 +/- 0.83 kcal/mol | 490 | -12.35 kcal/mol\n 15 | 377698 | -9.53 kcal/mol | -11.80 +/- 0.42 kcal/mol | 52 | -12.62 kcal/mol\n 20 | 505556 | -11.68 kcal/mol | -12.42 +/- 0.18 kcal/mol | 14 | -12.67 kcal/mol\n 25 | 633659 | -12.23 kcal/mol | -12.48 +/- 0.19 kcal/mol | 16 | -12.74 kcal/mol\n 30 | 762773 | -12.30 kcal/mol | -12.59 +/- 0.14 kcal/mol | 16 | -12.83 kcal/mol\n 35 | 892173 | -12.46 kcal/mol | -12.73 +/- 0.15 kcal/mol | 14 | -13.02 kcal/mol\n 40 | 1021489 | -12.57 kcal/mol | -12.81 +/- 0.13 kcal/mol | 14 | -13.05 kcal/mol\n 45 | 1151059 | -12.68 kcal/mol | -12.83 +/- 0.11 kcal/mol | 19 | -13.06 kcal/mol\n 50 | 1280984 | -12.74 kcal/mol | -12.84 +/- 0.10 kcal/mol | 13 | -13.07 kcal/mol\nDocking time 3.916151s\n------------+--------------+------------------+------------------------------+---------+-------------------\n Finished evaluation after reaching\n -12.80 +/- 0.13 kcal/mol combined.\n 60 samples, best energy -13.07 kcal/mol.\nShutdown time 0.000570s\nJob #0 took 3.955 sec after waiting 3.373 sec for setup\n(Thread 0 is processing Job 0)\nRun time of entire job set (1 files): 8.413 sec\nSavings from multithreading: -2.039 sec\nIdle time of execution thread: 4.458 sec\nAll jobs ran without errors.\n```\n\n## Interactive shell with docker\nThe following command will launch an interactive shell in the AutoDock-GPU\ncontainer using `docker` mounting `$HOME/data` from the underlying\nsystem as `/data` in the container:\n\n```\n$ docker run -it --rm --gpus all -v $HOME/data:/data --workdir /data nvcr.io/hpc/autodock:2020.06\n```\n\nWhere:\n* `-it`: start the container with an interactive terminal (short for --interactive --tty)\n* `--rm`: make container ephemeral (removes container on exit)\n* `-v $(pwd):/host_pwd`: bind mount the current working directory into the container as `/host_pwd`\n* `--gpus all`: vanilla docker GPU provisioning\n* `--workdir /data`: sets working directory inside the container\n \nThis should produce a root prompt within the container:\n\n```bash\nroot@3a8c8b7c3a88:/data#\n```\n\n# Running with Singularity\n\n## Pull the image\nSave the NGC AutoDock-GPU container as a local Singularity image file:\n```\n$ singularity build autodock.simg docker://nvcr.io/hpc/autodock:2020.06\n```\nThe AutoDock-GPU Singularity image is now saved in the current directory as `autodock.simg`\n\n### Note: Singularity/2.x\nIn order to pull NGC images with `singularity` version 2.x and earlier, NGC container registry authentication credentials are required.\n\nTo set your NGC container registry authentication credentials:\n```bash\n$ export SINGULARITY_DOCKER_USERNAME='$oauthtoken'\n$ export SINGULARITY_DOCKER_PASSWORD=\n```\n\nMore information describing how to obtain and use your NVIDIA NGC Cloud Services API key can be found\n[here](http://docs.nvidia.com/ngc/ngc-user-guide/index.html).\n\n### Note: Singularity 3.1.x - 3.2.x\nThere is currently a bug in Singularity 3.1.x and 3.2.x causing the `LD_LIBRARY_PATH` to be incorrectly set within the container environment.\nAs a workaround The `LD_LIBRARY_PATH` must be unset before invoking Singularity:\n```bash\n$ LD_LIBRARY_PATH=\"\" singularity exec ...\n```\n\n## Command line execution\n\nIn order to run a test benchmark: download, permission, and run the example script from the [NGC Examples Repository](https://gitlab.com/NVHCP/ngc-examples/).\n\n``` bash\nwget ...\nchmod +x check.py\nsingularity run --nv -B ${PWD}:/host_pwd --pwd /host_pwd ./check.py\n```\n\nExample of successful AutoDock-GPU output:\n\n``` bash\nAutoDock-GPU version: 09773678fc7e39677061d765b767f4bae8930fb7-dirty\nCUDA Setup time 2.031275s\n(Thread 0 is setting up Job 0)\nWarning: unknown argument '-maxnev'.\nRunning Job #0:\nUsing heuristics: -lsmet sw -nev 7468504\nLocal-search chosen method is: swSolis-Wets (sw)\nRest of Setup time 0.037800s\nExecuting docking runs, stopping automatically after either reaching 0.15 kcal/mol standard deviation of\nthe best molecules of the last 4 * 5 generations, 27000 generations, or 7468504 evaluations:\nGenerations | Evaluations | Threshold | Average energy of best 10% | Samples | Best energy\n------------+--------------+------------------+------------------------------+---------+-------------------\n 0 | 150 | -2.91 kcal/mol | -4.10 +/- 0.40 kcal/mol | 5 | -4.82 kcal/mol\n 5 | 125155 | -2.91 kcal/mol | -5.70 +/- 1.40 kcal/mol | 8066 | -11.91 kcal/mol\n 10 | 250790 | -5.69 kcal/mol | -9.55 +/- 0.83 kcal/mol | 490 | -12.35 kcal/mol\n 15 | 377698 | -9.53 kcal/mol | -11.80 +/- 0.42 kcal/mol | 52 | -12.62 kcal/mol\n 20 | 505556 | -11.68 kcal/mol | -12.42 +/- 0.18 kcal/mol | 14 | -12.67 kcal/mol\n 25 | 633659 | -12.23 kcal/mol | -12.48 +/- 0.19 kcal/mol | 16 | -12.74 kcal/mol\n 30 | 762773 | -12.30 kcal/mol | -12.59 +/- 0.14 kcal/mol | 16 | -12.83 kcal/mol\n 35 | 892173 | -12.46 kcal/mol | -12.73 +/- 0.15 kcal/mol | 14 | -13.02 kcal/mol\n 40 | 1021489 | -12.57 kcal/mol | -12.81 +/- 0.13 kcal/mol | 14 | -13.05 kcal/mol\n 45 | 1151059 | -12.68 kcal/mol | -12.83 +/- 0.11 kcal/mol | 19 | -13.06 kcal/mol\n 50 | 1280984 | -12.74 kcal/mol | -12.84 +/- 0.10 kcal/mol | 13 | -13.07 kcal/mol\nDocking time 3.916151s\n------------+--------------+------------------+------------------------------+---------+-------------------\n Finished evaluation after reaching\n -12.80 +/- 0.13 kcal/mol combined.\n 60 samples, best energy -13.07 kcal/mol.\nShutdown time 0.000570s\nJob #0 took 3.955 sec after waiting 3.373 sec for setup\n(Thread 0 is processing Job 0)\nRun time of entire job set (1 files): 8.413 sec\nSavings from multithreading: -2.039 sec\nIdle time of execution thread: 4.458 sec\nAll jobs ran without errors.\n```\n\n## Interactive shell\n\nThe following command will launch an interactive shell in the AutoDock-GPU\ncontainer using `singularity shell`:\n\n``` bash\n$ singularity shell --nv -B \n```\n\nWhere:\n* `--nv`: expose the host GPU(s) to the container\n* `-B`: bind user-defined directory into the container\n\nThis should produce a Singularity shell prompt within the container:\n\n``` bash\nSingularity: Invoking an interactive shell within container...\n\nSingularity Autodock.simg:~/example>\n```\n\n# Suggested Reading\n[Autodock main page](https://github.com/ccsb-scripps/AutoDock-GPU)\n[Autodock wiki](https://github.com/ccsb-scripps/AutoDock-GPU/wiki)\n[Autodock officional github page](https://ccsb.scripps.edu/autodock/)","AutoDock-GPU",["D",1592807586978],"Scripps",[192,248,245],"/api/image/hpc/-/containers/autodock",82847185,"hasSignedTag",[],[270,271,272],[],[],[],{"_92":278,"_125":278,"_126":279,"_128":129},{"_92":275,"_125":276,"_126":277,"_128":129},{"_92":273,"_125":273,"_126":274,"_128":129},"x86_64",{"_130":129,"_125":273},"HPC","pltfm_hpc",{"_130":134,"_125":276},"arm64",{"_130":129,"_125":278},{"_160":1452,"_162":1453},"ngcApps",[1405],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1386":1387,"_1388":1389,"_1390":1391,"_1392":1393,"_1394":291,"_191":315,"_1395":1396,"_1397":51,"_1398":140,"_1399":140,"_1400":1401,"_1402":140,"_1403":1404},"amplitude",{"_1383":1384,"_989":-7,"_1385":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1377":1378,"_1379":1380,"_1381":1382},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.2","defaultSearchSuggestions",{"_1264":396,"_1265":1266,"_1267":1268,"_1269":1270},"announcements",[],"consentState",{"_353":1254,"_1255":26,"_1256":26,"_1257":140,"_1258":1259},"consentConfig",{"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354},"showContactAdmin",1783520715782,[],[],[329,330,331,332,333,334],{"_40":62,"_42":67,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":62,"_42":65,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":62,"_42":63,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":54,"_50":51},{"_40":41,"_42":43,"_44":64,"_46":-5,"_48":-5,"_50":51},"mainInfoText","We 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These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648],[593,594,595,596,597],{"_394":487,"_92":487,"_91":396,"_367":619},{"_394":453,"_92":453,"_91":396,"_367":614},{"_394":607,"_92":607,"_91":396,"_367":608},{"_394":603,"_92":603,"_91":396,"_367":604},{"_394":598,"_92":598,"_91":396,"_367":599},"nvcrm.my.site.com",[600],{"_246":601,"_401":598,"_402":26,"_91":602},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[605],{"_246":606,"_401":603,"_402":26,"_91":436},"edge","github.com",[609,610],{"_246":613,"_401":607,"_402":26,"_91":612},{"_246":611,"_401":607,"_402":26,"_91":612},"logged_in","This domain is owned by Github. 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