3370478
doi
10.5281/zenodo.3370478
oai:zenodo.org:3370478
Rodríguez-Pérez, Raquel
Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, D-53115 Bonn, Germany.
Bajorath, Jürgen
Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, D-53115 Bonn, Germany.
Machine Learning Models for Predicting Kinase Inhibitors with Different Binding Modes
Miljković, Filip
Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, D-53115 Bonn, Germany.
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
protein kinases
kinase inhibitors
machine learning
inhibitor binding modes
classification models
X-ray data
<p>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.</p>
<p>Please refer to READ_ME.txt for more information.</p>
Zenodo
2019-08-18
info:eu-repo/semantics/other
3370477
1586291346.477514
386388865
md5:981d640862e7c5bb485e30ecfd8fff8d
https://zenodo.org/records/3370478/files/ML_Kinases.zip
public
10.5281/zenodo.3370477
isVersionOf
doi