ArticleDrug discovery today2015
Quantitative high-throughput screening data analysis: challenges and recent advances.
Article in Drug discovery today, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- A review of machine learning in toxicology: current practices and reporting gaps.Archives of toxicology · 2026Review
- Ex vivo drug screening and clustering of bladder cancers for pre-clinical treatment prediction.Communications medicine · 2026Article
- PLASMOpred: A Machine Learning-Based Web Application for Predicting Antimalarial Small Molecules Targeting the Apical Membrane Antigen 1-Rhoptry Neck Protein 2 Invasion Complex.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Binding Activity Classification of Anti-SARS-CoV-2 Molecules using Deep Learning Across Multiple AssaysBalkan medical journal · 2024Article
- AI in drug discovery and its clinical relevance.Heliyon · 2023Review
- Accounting for Artifacts in High-Throughput Toxicity Assays.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Chemoinformatic Analysis of Psychotropic and Antihistaminic Drugs in the Light of Experimental Anti-SARS-CoV-2 Activities.Advances and applications in bioinformatics and chemistry : AABC · 2021Article
- Rethinking drug design in the artificial intelligence era.Nature reviews. Drug discovery · 2020Review
- Quality Control of Quantitative High Throughput Screening Data.Frontiers in genetics · 2019Article
- Dose-Related Severity Sequence, and Risk-Based Integration, of Chemically Induced Health Effects.Toxicological sciences : an official journal of the Society of Toxicology · 2018Article
- Identification of small molecule inhibitors targeting the SMARCA2 bromodomain from a high-throughput screening assay.Acta pharmacologica Sinica · 2018Article
- Article
- Comparison of Points of Departure for Health Risk Assessment Based on High-Throughput Screening Data.Environmental health perspectives · 2017Article
- Estimating Potency in High-Throughput Screening Experiments by Maximizing the Rate of Change in Weighted Shannon Entropy.Scientific reports · 2016Article
- A Data Analysis Pipeline Accounting for Artifacts in Tox21 Quantitative High-Throughput Screening Assays.Journal of biomolecular screening · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
In vitro HTS holds much potential to advance drug discovery and provide cell-based alternatives for toxicity testing. In quantitative HTS, concentration-response data can be generated simultaneously for thousands of different compounds and mixtures. However, nonlinear modeling in these multiple-concentration assays presents important statistical challenges that are not problematic for linear models. The uncertainty of parameter estimates obtained from the widely used Hill equation model can be extremely large when using standard designs. Failure to properly consider standard errors of these parameter estimates would greatly hinder chemical genomics and toxicity testing efforts. In this light, optimal study designs should be developed to improve nonlinear parameter estimation; or alternative approaches with reliable performance characteristics should be used to describe concentration-response profiles.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.