Chebifier is a tool for automated classification of chemicals in the ChEBI ontology. This repository only hosts the front end of Chebifier. For the classification itself, see python-chebifier.
- 2025/11/05: Added new models (v244, including GAT, 3-STAR models and augmented GNNs), redesigned frontend.
- 2025/10/01: Fixed issue where server crashed if running predict without adding a SMILES string.
- 2025/10/01: Improved loading times significantly by only passing ChEBI-related information when needed.
Some dependencies require that pytorch is already installed:
pip install torch
After that, you can install the prediction system and web framework:
pip install -r backend/requirements.txt
Chebifier comes with a number of mandatory configuration files. config.template.json contains a template for a Chebifier configuration. Copy the contents of this file
cp backend/config.template.json backend/config.json
and change the path for each setting according to your setup.
The ensemble can take any models that are implemented in python-chebifier. See the repository for example configurations. Common arguments for a model are:
type: one of the available MODEL_TYPES, e.g.electra,batch_size: Number of molecules that are passed to the model at once,target_labels_path: List of ChEBI classes (theclasses.txtfile that comes as part of a ChEB-AI dataset)classwise_weights_path(optional): Weights that should be assigned to each class (i.e., trust scores calculated on a validation set with this script
Change to the respective directory and build the node.js files
cd react-app
npm run build
You can now start the development server with
cd backend
flask run
The server should now run at localhost:5000
If you found Chebifier useful, please cite: Martin Glauer, Fabian Neuhaus, Simon Flügel, Marie Wosny, Till Mossakowski, Adel Memariani, Johannes Schwerdt and Janna Hastings "Chebifier: Automating Semantic Classification in ChEBI to Accelerate Data-driven Discovery."Digital Discovery, 2024, 3, 896.