This repository includes the data and the scripts to create the large-scale kinase dataset and the accompaning modelling presented in Large-scale modelling of sparse kinase activity data.
The kinases sets were obtained with dataset_creation.py
- retriving kinase data from Papyrus
- filtering out kinases with less than 200 (kinase200) or 1000 (kinase1000) data points
- two balanced splits without data leakage (random- and cluster-based) were applied to both sets
- creates train, validation, test set input files for model training and evaluation
The four kinase benchmark datasets constructed in this work are at data/datasets.
To validate our implementation of the pQSAR2.0 model from Martin, et al.'s Profile-QSAR 2.0: Kinase Virtual Screening Accuracy Comparable to Four-Concentration IC50s for Realistically Novel Compounds, we retrived their datasets from the SI (data/pQSAR).
The training and testing of all models (except pQSAR) for all dataset and splits were run with run_models.py. The pQSAR models were separetaly run with run_pQSAR.py
The hyperparameter optimisation of the Random Forest and Xgboost models is done on the fly in run_models.py. For the D-MPNN models, it was done separately once with run_cp_opt.py.
The Analysis.ipynb notebook contains all steps to reproduce figures and analysis from the paper.