This repository accompanies the paper BitterMatch: recommendation systems for matching molecules with bitter taste receptors.
Updated versions of the data can be found at BitterDB. Please cite both papers when using this repository.
The file requirements.txt lists the package requirements.
For convenience the notebooks were adapted for running also in Google Colab.
filling_the_gaps-train.ipynb- On a single train-test split, the notebook trains all BitterMatch models for filling the gaps, and demontrsates the results.filling_the_gaps-eval.ipynb- Using a pre-trained model the notebook allows to predict activations for unknown values in the association matrix.
new_ligands-train.ipynb- On a single train-test split, the notebook trains the BitterMatch model for new ligands and demonstrates the results.filling_the_gaps-eval.ipynb- Using a pre-trained model the notebook loads data for ligands that were not used at training (evaluation data) and predicts activations for them.
All notebooks use the file similarity.py, that includes the functions to calculate collaborative similarities and extract similarity based features.
The file preprocessing.py includes helper functions to load the data from external formats as detailed in the paper.