Literature DB >> 27063506

A comprehensive company database analysis of biological assay variability.

Christian Kramer1, Göran Dahl2, Christian Tyrchan3, Johan Ulander4.   

Abstract

Analysis of data from various compounds measured in diverse biological assays is a central part of drug discovery research projects. However, no systematic overview of the variability in biological assays has been published and judgments on assay quality and robustness of data are often based on personal belief and experience within the drug discovery community. To address this we performed a reproducibility analysis of all biological assays at AstraZeneca between 2005 and 2014. We found an average experimental uncertainty of less than a twofold difference and no technologies or assay types had higher variability than others. This work suggests that robust data can be obtained from the most commonly applied biological assays.
Copyright © 2016. Published by Elsevier Ltd.

Mesh:

Year:  2016        PMID: 27063506     DOI: 10.1016/j.drudis.2016.03.015

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  6 in total

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Authors:  Govindan Subramanian; Rajendran Vairagoundar; Scott J Bowen; Nicole Roush; Theresa Zachary; Christopher Javens; Tracey Williams; Ann Janssen; Andrea Gonzales
Journal:  RSC Med Chem       Date:  2020-01-10

2.  Data Mining Approach for Extraction of Useful Information About Biologically Active Compounds from Publications.

Authors:  Olga A Tarasova; Nadezhda Yu Biziukova; Dmitry A Filimonov; Vladimir V Poroikov; Marc C Nicklaus
Journal:  J Chem Inf Model       Date:  2019-09-10       Impact factor: 4.956

Review 3.  Matched Molecular Pair Analysis in Short: Algorithms, Applications and Limitations.

Authors:  Christian Tyrchan; Emma Evertsson
Journal:  Comput Struct Biotechnol J       Date:  2016-12-13       Impact factor: 7.271

4.  Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data.

Authors:  Alexios Koutsoukas; Keith J Monaghan; Xiaoli Li; Jun Huan
Journal:  J Cheminform       Date:  2017-06-28       Impact factor: 5.514

5.  QPHAR: quantitative pharmacophore activity relationship: method and validation.

Authors:  Stefan M Kohlbacher; Thierry Langer; Thomas Seidel
Journal:  J Cheminform       Date:  2021-08-09       Impact factor: 5.514

6.  Nonadditivity in public and inhouse data: implications for drug design.

Authors:  D Gogishvili; E Nittinger; C Margreitter; C Tyrchan
Journal:  J Cheminform       Date:  2021-07-02       Impact factor: 5.514

  6 in total

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