ArticleJournal of medicinal chemistry2023
Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds.
Article in Journal of medicinal chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- High-Throughput Screening Identifies Small-Molecule Inhibitors of the Tau-LRP1 Interaction.bioRxiv : the preprint server for biology · 2026Article
- Deep learning for assay nuisance compound detection using a gated co-attention graph embedding model (CAGE-Fusion).Journal of cheminformatics · 2026Article
- Discovery of SARS-CoV-2 Nsp14-Methyltransferase (MTase) Inhibitors by Harnessing Scaffold-Centric Exploration of the Ultra Large Chemical Space.ACS pharmacology & translational science · 2025Article
- E-GuARD: expert-guided augmentation for the robust detection of compounds interfering with biological assays.Journal of cheminformatics · 2025Article
- Meeting report of the 37th International Conference on Antiviral Research in Gold Coast, Australia, May 20-24, 2024, organized by the International Society for Antiviral Research.Antiviral research · 2024Article
- Computational Approach for the Development of pH-Selective PD-1/PD-L1 Signaling Pathway Inhibition in Fight with Cancer.Cancers · 2024Article
- Tackling assay interference associated with small molecules.Nature reviews. Chemistry · 2024Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Hits from high-throughput screening (HTS) of chemical libraries are often false positives due to their interference with assay detection technology. In response, we generated the largest publicly available library of chemical liabilities and developed "Liability Predictor," a free web tool to predict HTS artifacts. More specifically, we generated, curated, and integrated HTS data sets for thiol reactivity, redox activity, and luciferase (firefly and nano) activity and developed and validated quantitative structure-interference relationship (QSIR) models to predict these nuisance behaviors. The resulting models showed 58-78% external balanced accuracy for 256 external compounds per assay. QSIR models developed and validated herein identify nuisance compounds among experimental hits more reliably than do popular PAINS filters. Both the models and the curated data sets were implemented in "Liability Predictor," publicly available at https://liability.mml.unc.edu/. "Liability Predictor" may be used as part of chemical library design or for triaging HTS hits.
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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.