Retrospective Analysis of an Experimental High-Throughput Screening Data Set by Recursive Partitioning

2001 ◽  
Vol 3 (3) ◽  
pp. 267-277 ◽  
Author(s):  
A. Michiel van Rhee ◽  
Jon Stocker ◽  
David Printzenhoff ◽  
Chris Creech ◽  
P. Kay Wagoner ◽  
...  
Keyword(s):  
Retrospective Analysis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Recursive Partitioning ◽  
Data Set

scholarly journals Using Weighted Entropy to Rank Chemicals in Quantitative High-Throughput Screening Experiments

2013 ◽  
Vol 19 (3) ◽  
pp. 344-353 ◽  
Author(s):  
Keith R. Shockley
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Nonlinear Models ◽  
Hill Equation ◽  
Parameter Estimates ◽  
Chemical Library ◽  
Data Set ◽  
Screening Experiments ◽  
The Hill ◽  
Weighted Entropy

Quantitative high-throughput screening (qHTS) experiments can simultaneously produce concentration-response profiles for thousands of chemicals. In a typical qHTS study, a large chemical library is subjected to a primary screen to identify candidate hits for secondary screening, validation studies, or prediction modeling. Different algorithms, usually based on the Hill equation logistic model, have been used to classify compounds as active or inactive (or inconclusive). However, observed concentration-response activity relationships may not adequately fit a sigmoidal curve. Furthermore, it is unclear how to prioritize chemicals for follow-up studies given the large uncertainties that often accompany parameter estimates from nonlinear models. Weighted Shannon entropy can address these concerns by ranking compounds according to profile-specific statistics derived from estimates of the probability mass distribution of response at the tested concentration levels. This strategy can be used to rank all tested chemicals in the absence of a prespecified model structure, or the approach can complement existing activity call algorithms by ranking the returned candidate hits. The weighted entropy approach was evaluated here using data simulated from the Hill equation model. The procedure was then applied to a chemical genomics profiling data set interrogating compounds for androgen receptor agonist activity.

Data Analysis of High-Throughput Screening Results:  Application of Multidomain Clustering to the NCI Anti-HIV Data Set

2002 ◽  
Vol 45 (14) ◽  
pp. 3082-3093 ◽  
Author(s):  
Susan Y. Tamura ◽  
Patricia A. Bacha ◽  
Heather S. Gruver ◽  
Ruth F. Nutt
Keyword(s):  
Data Analysis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Data Set ◽  
Anti Hiv

The Communication of Hit Quality Using Natural History Visualizations (NHVs)

2021 ◽  
pp. 247255522110175
Author(s):  
Kathryn M. Nelson ◽  
Michael A. Walters
Keyword(s):  
Case Report ◽  
Natural History ◽  
Physicochemical Properties ◽  
Retrospective Analysis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Active Compounds ◽  
Historical Literature ◽  
Integral Component ◽  
Natural Histories

High-throughput screening (HTS) often yields a list of compounds that requires prioritization before further work is performed. Prioritization criteria typically include activity, selectivity, physicochemical properties, and other absolute or calculated measurements of compound “value.” One critical method of compound prioritization is often not discussed in published accounts of HTS. We have referred to this oft-overlooked metric as “compound natural history.” These natural histories are observational evaluations of how a compound has been reported in the historical literature or compound databases. The purpose of this work was to develop a useful natural history visualization (NHV) that could form a standard, important part of hit reporting and evaluation. In this case report, we propose an efficient and effective NHV that will assist in the prioritization of active compounds and demonstrate its utility using a retrospective analysis of reported hits. We propose that this method of compound natural history evaluation be adopted in HTS triage and become an integral component of published reports of HTS outcomes.

Hormesis in high-throughput screening of antibacterial compounds in E coli

2010 ◽  
Vol 29 (8) ◽  
pp. 667-677 ◽  
Author(s):  
Edward J Calabrese ◽  
George R Hoffmann ◽  
Edward J Stanek ◽  
Marc A Nascarella
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Antibacterial Agents ◽  
Threshold Model ◽  
Concentration Effects ◽  
Data Set ◽  
E Coli ◽  
Model Predictions ◽  
High Throughput Study ◽  
Repeat Measure

This article assesses the response below a toxicological threshold for 1888 antibacterial agents in Escherichia coli, using 11 concentrations with twofold concentration spacing in a high-throughput study. The data set had important strengths such as low variability in the control (2%—3% SD), a repeat measure of all wells, and a built-in replication. Bacterial growth at concentrations below the toxic threshold is significantly greater than that in the controls, consistent with a hormetic concentration response. These findings, along with analyses of published literature and complementary evaluations of concentration-response model predictions of low-concentration effects in yeast, indicate a lack of support for the broadly and historically accepted threshold model for responses to concentrations below the toxic threshold.

scholarly journals Outlier Mining in High Throughput Screening Experiments

2002 ◽  
Vol 7 (4) ◽  
pp. 341-351 ◽  
Author(s):  
Michael F.M. Engels ◽  
Luc Wouters ◽  
Rudi Verbeeck ◽  
Greet Vanhoof
Keyword(s):  
Data Mining ◽  
Biological Activity ◽  
High Throughput ◽  
High Throughput Screening ◽  
False Negative ◽  
Data Sets ◽  
Data Set ◽  
Structure Information ◽  
Screening Experiments ◽  
Mining Procedure

A data mining procedure for the rapid scoring of high-throughput screening (HTS) compounds is presented. The method is particularly useful for monitoring the quality of HTS data and tracking outliers in automated pharmaceutical or agrochemical screening, thus providing more complete and thorough structure-activity relationship (SAR) information. The method is based on the utilization of the assumed relationship between the structure of the screened compounds and the biological activity on a given screen expressed on a binary scale. By means of a data mining method, a SAR description of the data is developed that assigns probabilities of being a hit to each compound of the screen. Then, an inconsistency score expressing the degree of deviation between the adequacy of the SAR description and the actual biological activity is computed. The inconsistency score enables the identification of potential outliers that can be primed for validation experiments. The approach is particularly useful for detecting false-negative outliers and for identifying SAR-compliant hit/nonhit borderline compounds, both of which are classes of compounds that can contribute substantially to the development and understanding of robust SARs. In a first implementation of the method, one- and two-dimensional descriptors are used for encoding molecular structure information and logistic regression for calculating hits/nonhits probability scores. The approach was validated on three data sets, the first one from a publicly available screening data set and the second and third from in-house HTS screening campaigns. Because of its simplicity, robustness, and accuracy, the procedure is suitable for automation.

scholarly journals Artificial Neural Network—Based Analysis of High-Throughput Screening Data for Improved Prediction of Active Compounds

2009 ◽  
Vol 14 (10) ◽  
pp. 1236-1244 ◽  
Author(s):  
Swapan Chakrabarti ◽  
Stan R. Svojanovsky ◽  
Romana Slavik ◽  
Gunda I. Georg ◽  
George S. Wilson ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Back Propagation ◽  
Large Data ◽  
Classification Performance ◽  
Data Sets ◽  
Data Set ◽  
Active Compounds ◽  
Physicochemical Features ◽  
Artificial Neural

Artificial neural networks (ANNs) are trained using high-throughput screening (HTS) data to recover active compounds from a large data set. Improved classification performance was obtained on combining predictions made by multiple ANNs. The HTS data, acquired from a methionine aminopeptidases inhibition study, consisted of a library of 43,347 compounds, and the ratio of active to nonactive compounds, R A/N, was 0.0321. Back-propagation ANNs were trained and validated using principal components derived from the physicochemical features of the compounds. On selecting the training parameters carefully, an ANN recovers one-third of all active compounds from the validation set with a 3-fold gain in R A/N value. Further gains in RA/N values were obtained upon combining the predictions made by a number of ANNs. The generalization property of the back-propagation ANNs was used to train those ANNs with the same training samples, after being initialized with different sets of random weights. As a result, only 10% of all available compounds were needed for training and validation, and the rest of the data set was screened with more than a 10-fold gain of the original RA/N value. Thus, ANNs trained with limited HTS data might become useful in recovering active compounds from large data sets.

Sign in / Sign up

Export Citation Format

Share Document