engleski

Prediction of Drug-Kinase Binding Affinities with Focus on Conserved Protein Kinase Domain

Previous approaches implemented on drug-kinase binding affinity benchmark datasets show poor performance on rigorous test scenarios with unseen small compounds or protein kinase targets - thus limiting their real-world application. We represent methodology which relies on an ensemble approach, XGBoost trained on fingerprint based and protein kinase domain sequence-based similarity features - and graph convolutional networks (GCN) as more advanced representation learning predictive methodology. To assess the uncertainty surrounding model predictions, we defined a structure-based applicability domain with a focus on density of compound space in the training set.

kinase ; binding affinity

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