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Analysis and modelling of false positives in GPCR assays.
Lect. Notes Comput. Sc. 11731 LNCS, 764-770 (2019)
G-Protein Coupled Receptors (GPCR) are involved in all the major signaling pathways. As a result, they often serve as potential target for therapeutic drugs. In this study we analyze publicly available assays involving different classes of GPCR to identify false positives. Using the latest developments in Machine Learning, we then build models that can predict such compounds with high confidence. Given the ubiquity of GPCR assays, we believe such models will be very helpful in flagging potential false positives for further testing.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Frequent Hitters ; Least Squares Svm ; Neural Networks
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Quellenangaben
Volume: 11731 LNCS,
Pages: 764-770
Publisher
Springer
Publishing Place
Berlin [u.a.]
Non-patent literature
Publications
Institute(s)
Institute of Structural Biology (STB)