Usage

Prerequisites

Drug Sniffer is implemented as a Nextflow workflow. Users will need to install Nextflow before they can use Drug Sniffer, it is generally quite easy to install, see the website for details.

Note that when Nextflow is installed using the default method, a file called nextflow is created. This can be moved to a location on the user’s PATH or it can be invoked like ./nextflow, per standard Unix practices.

Docker also must be installed in the execution environment and configured so that the user launching the Nextflow workflow has permission to run Docker containers. In the future, we intend to support Singularity containers as well since this container runtime is more commonly available in HPC environments.

Containers

The first step is to build the Docker images. This can be done by running the build-images.sh script in the tool/ directory.

./tool/build-images.sh

This will build the necessary Docker images. If the workflow is to be run on a cluster or cloud environment then it may be necessary to push the images to a registry. In this case, set the IMAGE_NAMESPACE environment variable to a valid registry and namespace when running the script above.

IMAGE_NAMESPACE=fancyregistry.io/mylab ./tool/build-images.sh

If the IMAGE_PUSH environment variable is set to anything other than 0 (the default), the images will also be pushed to the specified registry, which defaults to docker.io is otherwise unspecified.

IMAGE_NAMESPACE=mylab IMAGE_PUSH=1 ./tool/build-images.sh

Running

The simplest way to learn how to use Drug Sniffer is to experiment with the examples (see below for more information). These may be found in the examples/ directory within the project repository. First, clone the repository (https://github.com/TravisWheelerLab/drug-sniffer). Then, from the project root directory, you can run one of the examples with the command below.

nextflow run -profile local -params-file examples/3vri_params.yaml .

There are three things going on here. First, we select the environment the workflow will run in with -profile local. The available environments are described in nextflow.config in the same directory. An example is shown below:

profiles {
  local {
      process.executor = 'local'
      docker.enabled = true
  }

  aws_batch {
      process.executor = 'awsbatch'
      process.queue = 'drug-sniffer-queue'
      aws.region = 'us-east-1'
  }
}

The config above describes two environments. The first, local, which runs the workflow on the local machine, with Docker enabled (Drug Sniffer requires Docker or another container runtime). The second is aws_batch, which will run the workflow in the AWS cloud using the Batch batch processing service, which would need to have been configured with a queue called drug-sniffer-queue.

See the Nextflow documentation for information about other environments, including SLURM. The configuration file format is also described.

Next, we specify a set of parameters for the workflow run with -params-file examples/3vri_params.yaml. This tells Nextflow to load workflow parameters from the specified YAML file. An example file is shown below:

See Parameters for details on the available workflow parameters.

molecule_db: '${projectDir}/../examples/small-db'
tanimoto_cutoff: 0.5

receptor_pdb: '${projectDir}/../examples/3vri_aligned.pdb'

receptor_center_x: 14.641000
receptor_center_y: -11.026000
receptor_center_z: 43.231998

receptor_size_x: 10.0
receptor_size_y: 10.0
receptor_size_z: 10.0

admet_checks: '1 2 3'

output_dir: '${launchDir}/drug-sniffer-output'

The parameters described in this file are explained on the Parameters page. Of interest, however, is the ${launchDir} variable, which is set to the directory from which the nextflow command is run (running a Nextflow workflow is often called “launching” it). There is also a variable called projectDir available which is set to the location of the workflow itself (the .nf file).

Finally, we tell Nextflow to run the workflow configured for the current directory (using .). It is also possible to run the workflow without cloning the Git repository by referencing the repo on the command line:

nextflow run -profile local -params-file my-params.yaml \
  -r main TravisWheelerLab/drug-sniffer

The -r option tells Nextflow which branch to use. In this case, our primary branch is called “main”, so that’s usually the one you want to execute.

We also suggest using the -with-report option to the Nextflow “run” command as it produces a useful report after the workflow has finished. See the example report for details.

Output

There are two output files. The first, all_errors.txt, contains errors produced during the workflow run. The second, all_results.txt contains the actual output. The output file is tab-separated and includes the fields listed below:

  1. Pose - the ID of the Autodock Vina pose

  2. Chemical name - the name of the chemical from the molecule database

  3. Chemical database - the name of the database that contains the chemical

  4. Chemical SMILES string - the raw SMILES string

  5. dock2bind score - the score assigned by the dock2bind model

  6. Three columns per ADMET check - predicted, confidence, and credibility, see the FPADMET documentation for more details

  7. The calculated logp value

Examples

There are two examples, both found in the examples/ directory within the repository: 3vri and 5l2s. The first, when run, will test a pre-computed set of ligands, effectively skipping Stage 3 of the pipeline and going right to Stage 4. This has two benefits. First, Autogrow4 takes a long time to run, so if the goal is to simply see the pipeline in action, or verify some change, the 3vri example is the way to go. Second, some users may want to create ligands to test using some other method, and the 3vri example serves to demonstrate how to do this. The 5l2s example runs the entire pipeline.

Molecule Database

Drug Sniffer requires a database of potential molecules in order to function. We provide a large, curated database for use by the public. The database is an aggregation of a number of existing databases intended for drug research, and each molecule includes a reference back to its original source for convenience.

The database is about 141GB compressed, so it requires a large filesystem. Further, when running Drug Sniffer on a cluster, we recommend that you make the database accessible through NFS or some similar means to avoid downloading it on to each node.

Once extracted, you can point Drug Sniffer at the location using the molecule_db parameter.

The full database is available for download at https://data.drugsniffer.org/molecules.zip.