ArticleCommunications biology2022
Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed.
- Identification of chemical features for improved outer membrane permeation in mycobacteria using machine learning.Nature microbiology · 2026Article
- Cell painting and thermal proteome profiling for inference of drug targets and mechanism of action.Molecular systems biology · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Deconstructing the RAGE signaling maze: the molecular key to opening a new dimension of ovarian anti-aging.Experimental & molecular medicine · 2026Review
- Counting cells can accurately predict small-molecule bioactivity benchmarks.Nature communications · 2026Article
- Leveraging Consensus Docking Approaches for Human Mitochondrial Complexes I and III.Chemical research in toxicology · 2026Article
- Non-invasive quantification of viability in liver spheroids using deep learning.Frontiers in bioengineering and biotechnology · 2026Article
- Computational prediction of mutagenicity through comprehensive cell painting analysis.Mutagenesis · 2025Article
- Big data and AI: Potential and challenges for digital transformation in toxicology.Environmental analysis, health and toxicology · 2025Article
- Mixture of experts for multitask learning in cardiotoxicity assessment.Journal of cheminformatics · 2025Article
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- VitroBert: modeling DILI by pretraining BERT on in vitro data.Journal of cheminformatics · 2025Article
- Cross modality learning of cell painting and transcriptomics data improves mechanism of action clustering and bioactivity modelling.Scientific reports · 2025Article
- Intersecting impact of CAG repeat and huntingtin knockout in stem cell-derived cortical neurons.Neurobiology of disease · 2025Article
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- Bioactivity of the ubiquitous tire preservative 6PPD and degradant, 6PPD-quinone in fish- and mammalian-based assays.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
- Machine Learning-Enabled Drug-Induced Toxicity Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Exploring NLRP3-related phenotypic fingerprints in human macrophages using Cell Painting assay.iScience · 2025Article
- Cell Painting: a decade of discovery and innovation in cellular imaging.Nature methods · 2025Review
- Progress in toxicogenomics to protect human health.Nature reviews. Genetics · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mitochondrial toxicity is an important safety endpoint in drug discovery. Models based solely on chemical structure for predicting mitochondrial toxicity are currently limited in accuracy and applicability domain to the chemical space of the training compounds. In this work, we aimed to utilize both -omics and chemical data to push beyond the state-of-the-art. We combined Cell Painting and Gene Expression data with chemical structural information from Morgan fingerprints for 382 chemical perturbants tested in the Tox21 mitochondrial membrane depolarization assay. We observed that mitochondrial toxicants differ from non-toxic compounds in morphological space and identified compound clusters having similar mechanisms of mitochondrial toxicity, thereby indicating that morphological space provides biological insights related to mechanisms of action of this endpoint. We further showed that models combining Cell Painting, Gene Expression features and Morgan fingerprints improved model performance on an external test set of 244 compounds by 60% (in terms of F1 score) and improved extrapolation to new chemical space. The performance of our combined models was comparable with dedicated in vitro assays for mitochondrial toxicity. Our results suggest that combining chemical descriptors with biological readouts enhances the detection of mitochondrial toxicants, with practical implications in drug discovery.
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