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Machine Learning Models for Predicting Kinase Inhibitors with Different Binding Modes

Name: Machine Learning Models for Predicting Kinase Inhibitors with Different Binding Modes
Keywords: protein kinases, machine learning, classification models, kinase inhibitors, inhibitor binding modes, X-ray data

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 18 Aug 2019Publisher:Zenodo

Authors: Miljković, Filip; Rodríguez-Pérez, Raquel; Bajorath, Jürgen;

doi: 10.5281/zenodo.3370477 , 10.5281/zenodo.3370478 , 10.5281/zenodo.3743636

Machine Learning Models for Predicting Kinase Inhibitors with Different Binding Modes

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Abstract

Random forest (RF), support-vector machine (SVM), and deep neural network (DNN) models for predicting kinase inhibitors with different binding modes in X-ray structures are made available together with the data sets used for training and testing. Please refer to READ_ME.txt for more information.

Related Organizations

Lee University
United States
University of Bonn
Germany

Keywords

protein kinases, machine learning, classification models, kinase inhibitors, inhibitor binding modes, X-ray data

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