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Ghosh, D.* ; Koch, U.* ; Hadian, K. ; Sattler, M. ; Tetko, I.V.

Highly accurate filters to flag frequent hitters in alphascreen assays by suggesting their mechanism.

Mol. Inform., DOI: 10.1002/minf.202100151:e2100151 (2021)
Postprint DOI PMC
Open Access Green
AlphaScreen is one of the most widely used assay technologies in drug discovery due to its versatility, dynamic range and sensitivity. However, a presence of false positives and frequent hitters contributes to difficulties with an interpretation of measured HTS data. Although filters do exist to identify frequent hitters for AlphaScreen, they are frequently based on privileged scaffolds. The development of such filters is time consuming and requires deep domain knowledge. Recently, machine learning and artificial intelligence methods are emerging as important tools to advance drug discovery and chemoinformatics, including their application to identification of frequent hitters in screening assays. However, the relative performance and complementarity of the Machine Learning and scaffold-based techniques has not yet been comprehensively compared. In this study, we analysed filters based on the privileged scaffolds with filters built using machine learning. Our results demonstrate that machine-learning methods provide more accurate filters for identification of frequent hitters in AlphaScreen assays than scaffold-based methods and can be easily redeveloped once new data are measured. We present highly accurate models to identify frequent hitters in AlphaScreen assays.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Alphascreen ; False Positives ; Frequent Hitters ; High Throughput Assays ; Machine Learning ; Ochem; Neural-networks; Identification; Toxicity
ISSN (print) / ISBN 1868-1743
e-ISSN 1868-1751
Zeitschrift Molecular Informatics
Quellenangaben Band: , Heft: , Seiten: , Artikelnummer: e2100151 Supplement: ,
Verlag Wiley
Verlagsort Weinheim
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Structural Biology (STB)
Institute of Molecular Toxicology and Pharmacology (TOXI)
Förderungen Ministry of Science and Higher Education of The Russian Federation
European Union