Assessing the information content of structural and protein–ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning

Assessing the information content of structural and protein–ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning

Abstract For kinase inhibitors, X-ray crystallography has revealed different types of binding modes. Currently, more than 2000 kinase inhibitors with known binding modes are available, which makes it possible to derive and test machine learning models for the prediction of inhibitors with different...

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Bibliographic Details
Main Authors: Raquel Rodríguez-Pérez, Filip Miljković, Jürgen Bajorath
Format: Article
Language:English
Published: BMC 2020-05-01
Series:Journal of Cheminformatics
Subjects:
Active learning
Machine learning
Atom environment fingerprints
Interaction fingerprints
Kinase inhibitors
X-ray structures
Online Access:http://link.springer.com/article/10.1186/s13321-020-00434-7

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