Downloads provided by UsageCountsMachine Learning Models for Predicting Kinase Inhibitors with Different Binding Modes
Miljković, Filip; Rodríguez-Pérez, Raquel; Bajorath, Jürgen;
Machine Learning Models for Predicting Kinase Inhibitors with Different Binding Modes
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.
protein kinases, machine learning, classification models, kinase inhibitors, inhibitor binding modes, X-ray data
protein kinases, machine learning, classification models, kinase inhibitors, inhibitor binding modes, X-ray data
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average views 19 downloads 17 - 19views17downloads
selected citations
Citations provided by BIP!
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP!influenceInfluence provided by BIP!
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Influence provided by BIP!impulseImpulse provided by BIP!
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
Impulse provided by BIP!viewsViews provided by UsageCounts
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