Detection of Compounds Interfering with HTS Assay Technologies.

Clicks: 121
ID: 33213
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #20 of 37 articles by views in ChemMedChem

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
A significant challenge in High-Throughput Screening (HTS) campaigns is the identification of assay technology interference compounds. A Compound Interfering with the Assay Technology (CIAT) gives false readouts in many assays. CIATs are often considered viable hits and investigated in follow-up studies, thus impeding research and wasting resources. In this study, we developed a machine-learning (ML) model to predict CIATs for three assay technologies. The model was trained on known CIATs and non-CIATs (NCIATs) identified in artefact assays and described by their 2D structural descriptors. Usual methods identifying CIATs are based on statistical analysis of historical primary screening data and do not consider experimental assays identifying CIATs. Our results show successful prediction of CIATs for existing and novel compounds and provide a complementary and wider set of predicted CIATs compared to BSF, a published structure-independent model, and to the PAINS substructural filters. Our analysis is an example of how well-curated datasets can provide powerful predictive models despite their relatively small size.
Reference Key
david2019detectionchemmedchem Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors David, Laurianne;Walsh, Jarrod;Noé, Sturm;Isabella, Feierberg;Nissink, Willem;Chen, Hongming;Bajorath, Jürgen;Engkvist, Ola;
Journal ChemMedChem
Year 2019
DOI
10.1002/cmdc.201900395
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.