ArticleChemMedChem2019
Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies.
Article in ChemMedChem, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 34 citations in OpenAlex.
- Absolute Binding Free Energy Calculations between the SARS-CoV-2 Main Protease and 130 Drug Leads Using Implicit Ligand Theory.Journal of chemical information and modeling · 2026Article
- E-GuARD: expert-guided augmentation for the robust detection of compounds interfering with biological assays.Journal of cheminformatics · 2025Article
- A Collection of Useful Nuisance Compounds (CONS) for Interrogation of Bioassay Integrity.JACS Au · 2024Article
- Machine Learning-Driven Data Valuation for Optimizing High-Throughput Screening Pipelines.Journal of chemical information and modeling · 2024Article
- Chasing Red Herrings: Palladium Metal Salt Impurities Feigning KRAS Activity in Biochemical Assays.Journal of medicinal chemistry · 2024Article
- Synergizing Chemical Structures and Bioassay Descriptions for Enhanced Molecular Property Prediction in Drug Discovery.Journal of chemical information and modeling · 2024Article
- Tackling assay interference associated with small molecules.Nature reviews. Chemistry · 2024Review
- Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery.ACS central science · 2024Article
- Determination of pharmacological inhibition of ALDH2 by ethanol clearance in mice.Toxicology and applied pharmacology · 2024Article
- Tuning gradient boosting for imbalanced bioassay modelling with custom loss functions.Journal of cheminformatics · 2022Article
- Gains from no real PAINS: Where 'Fair Trial Strategy' stands in the development of multi-target ligands.Acta pharmaceutica Sinica. B · 2021Review
- A High-Throughput Assay to Identify Allosteric Inhibitors of the PLC-γ Isozymes Operating at Membranes.Biochemistry · 2020Article
- Statistical models for identifying frequent hitters in high throughput screening.Scientific reports · 2020Article
- Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies.ChemMedChem · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 4 countries.
Funding
Abstract
A significant challenge in high-throughput screening (HTS) campaigns is the identification of assay technology interference compounds. A Compound Interfering with an 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.
Indexed as
Identifiers
What OpenQuestion holds
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.