Machine Learning Models for Accurate Prediction of Kinase Inhibitors with Different Binding Modes.

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ID: 27437
2019
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Abstract
Noncovalent inhibitors of protein kinases have different modes of action. They bind to the active or inactive form of kinases, compete with ATP, stabilize inactive kinase conformations, or act through allosteric sites. Accordingly, kinase inhibitors have been classified on the basis of different binding modes. For medicinal chemistry, it would be very useful to derive mechanistic hypotheses for newly discovered inhibitors. Therefore, we have applied different machine learning approaches to generate models for predicting different classes of kinase inhibitors including types I, I/, and II as well as allosteric inhibitors. These models were built on the basis of compounds with binding modes confirmed by X-ray crystallography and yielded unexpectedly accurate and stable predictions without the need for deep learning. The results indicate that the new machine learning models have considerable potential for practical applications. Therefore, our data sets and models are made freely available.
Reference Key
miljkovi2019machinejournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Miljković, Filip;Rodríguez-Pérez, Raquel;Bajorath, Jürgen;
Journal Journal of medicinal chemistry
Year 2019
DOI
10.1021/acs.jmedchem.9b00867
URL
Keywords Keywords not found

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