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Ghosh, D.* ; Tetko, I.V. ; Klebl, B.* ; Nussbaumer, P.* ; Koch, U.*

Analysis and modelling of false positives in GPCR assays.

Lect. Notes Comput. Sc. 11731 LNCS, 764-770 (2019)
Verlagsversion DOI
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Frequent Hitters ; Least Squares Svm ; Neural Networks
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 11731 LNCS, Heft: , Seiten: 764-770 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute of Structural Biology (STB)