Software package for predicting synthetic accesibility of organic molecules. The tools are deployed as a Flask webapp, using PostgreSQL database.
This code was developed by Steven Bennett, while undergoing my PhD in the Jelfs group, jelfs-group.org.
- Install Docker OS specific instructions..
- Install docker-compose.
The RMM image can be built using the Dockerfile in the repository flask/Dockerfile
. This will build the Flask image to the local machine, however, database-based operations will be unable to be run, if the PostgreSQL container is not running.
The docker-compose.yml
contains the configuation to deploy the full-stack. This does not require that the RMM Flask image is has been built. To build and run the initial database run
docker-compose build database
docker-compose up -d database
This will run the database
Docker image in the background, creating a user, example
, and database example
also. It will populate both of them with the SMILES molecules present.
To run the full-stack run
docker-compose build
docker-compose up
This will run the three images required to serve the website on localhost
, database
, flask
and nginx
.
If using this for production, specify a username and password for the PostgresSQL database in the database.conf
folder and remove the default secret key from the Dockerfile.
The SMILES strings displayed by default can be customised. These are located in the init.sql
file in databases, and will automatically add to the database.
Additionally, this website can be used for any type of binary classification problem as the question can be changed.
This can be customised in flask/main/index.html
.