ArticleiScience2024
A combined AI and cell biology approach surfaces targets and mechanistically distinct Inflammasome inhibitors.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- The AI drug revolution needs a revolution.npj drug discovery · 2025Article
- A morphology and secretome map of pyroptosis.Molecular biology of the cell · 2025Article
- AI discovery of TLR agonist-driven phenotypes reveals unique features of peripheral cells from healthy donors and ART-suppressed people living with HIV.Frontiers in immunology · 2025Article
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Authors and funding
15 authors.
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Abstract
Inflammasomes are protein complexes that mediate innate immune responses whose dysregulation has been linked to a spectrum of acute and chronic human conditions, which dictates therapeutic development that is aligned with disease variability. We designed a scalable, physiologic high-content imaging assay in human PBMCs that we analyzed using a combination of machine-learning and cell biology methods. This resulted in a set of biologically interpretable readouts that can resolve a spectrum of cellular states associated with inflammasome activation and inhibition. These methods were applied to a phenotypic screen that surfaced mechanistically distinct inflammasome inhibitors from an annotated 12,000 compound library. A set of over 100 inhibitors, including an array of Raf-pathway inhibitors, were validated in downstream functional assays. This approach demonstrates how complementary machine learning-based methods can be used to generate profiles of cellular states associated with different stages of complex biological pathways and yield compound and target discovery.
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Registered trials
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