QSPRpred-Docker

This GitHub repo is set up for the development of a Docker-based service on the QSPRpred tool github.com/CDDLeiden/QSPRpred. In the initial stage, the service will have prediction functionalities based on imported, pre-trained model(s).

Flask Application Deployment with Docker

This guide covers the setup and usage of a Flask application for predictions via a Docker container. The application provides both a user interface for interacting with the model and an API for programmatic access.

Building the Docker Image

To build the Docker image for the Flask application, use the following command:

docker build -t qspr_flask_image -f Dockerfile .

Running the Docker Container

Run the Docker container with the following command. This command also mounts a local directory to the container to make model files accessible:

docker run -d -p 5000:5000 --name qspr_flask_container -v $(pwd)/models:/usr/src/app/models  qspr_flask_image

Accessing the Application

Once the container is running, navigate to http://localhost:5000 in your web browser. You will see the Flask application interface which allows you to:

• Select one or more prediction models.
• Input multiple SMILES strings either through a text box or by uploading a CSV file (e.g. the smiles_sample.csv).
• Download the prediction results in various formats, or generate a report.

Using the API

You can also interact with the Flask application via its API from your coding environment. Below are examples of how to use the API:

Example: JSON Output

To initiate a prediction and receive the results in JSON format, use:

curl -X POST localhost:5000/api     -H "Content-Type: application/json"     -d '{
        "smiles": ["C1=CC=CC=C1C(=O)NC2=CC=CC=C2", "CC(=O)OC1=CC=CC=C1C(=O)O"],
        "models": ["P10827_RF_Model", "P10828_RF_Model"],
        "format": "text"
    }'

Example: CSV Output

To receive the prediction results as a CSV file, use:

curl -X POST localhost:5000/api     -H "Content-Type: application/json"     -d '{
        "smiles": ["C1=CC=CC=C1C(=O)NC2=CC=CC=C2", "CC(=O)OC1=CC=CC=C1C(=O)O"],
        "models": ["P10827_RF_Model", "P10828_RF_Model"],
        "format": "csv"
    }' -o predictions.csv