ArticleMolecular informatics2020
Using Machine Learning Methods and Structural Alerts for Prediction of Mitochondrial Toxicity.
Article in Molecular informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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.
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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
26 citing papers in PubMed, 58 citations in OpenAlex.
- Leveraging Consensus Docking Approaches for Human Mitochondrial Complexes I and III.Chemical research in toxicology · 2026Article
- Mixture of experts for multitask learning in cardiotoxicity assessment.Journal of cheminformatics · 2025Article
- Overview of Computational Toxicology Methods Applied in Drug and Green Chemical Discovery.Journal of xenobiotics · 2024Review
- Computational Strategies for Assessing Adverse Outcome Pathways: Hepatic Steatosis as a Case Study.International journal of molecular sciences · 2024Review
- Data mining of PubChem bioassay records reveals diverse OXPHOS inhibitory chemotypes as potential therapeutic agents against ovarian cancer.Journal of cheminformatics · 2024Article
- Improved Detection of Drug-Induced Liver Injury by Integrating PredictedChemical research in toxicology · 2024Article
- Improved Detection of Drug-Induced Liver Injury by Integrating PredictedbioRxiv : the preprint server for biology · 2024Article
- Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank.Journal of chemical information and modeling · 2024Article
- Article
- Unleashing the potential of cell painting assays for compound activities and hazards prediction.Frontiers in toxicology · 2024Review
- Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA DICTrank.bioRxiv : the preprint server for biology · 2023Article
- Complement System Inhibitory Drugs in a Zebrafish (International journal of molecular sciences · 2023Article
- In silico modeling-based new alternative methods to predict drug and herb-induced liver injury: A review.Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association · 2023Review
- Predicting the Mitochondrial Toxicity of Small Molecules: Insights from Mechanistic Assays and Cell Painting Data.Chemical research in toxicology · 2023Article
- Opportunities and challenges in application of artificial intelligence in pharmacology.Pharmacological reports : PR · 2023Review
- Quantitative assessment of mitochondrial morphology relevant for studies on cellular health and environmental toxicity.Computational and structural biotechnology journal · 2023Article
- Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection.Communications biology · 2022Article
- Cell Morphological Profiling Enables High-Throughput Screening for PROteolysis TArgeting Chimera (PROTAC) Phenotypic Signature.ACS chemical biology · 2022Article
- Prescription drugs and mitochondrial metabolism.Bioscience reports · 2022Review
- Mitochondrial toxicity evaluation of traditional Chinese medicine injections with a dualFrontiers in pharmacology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Over the last few years more and more organ and idiosyncratic toxicities were linked to mitochondrial toxicity. Despite well-established assays, such as the seahorse and Glucose/Galactose assay, an in silico approach to mitochondrial toxicity is still feasible, particularly when it comes to the assessment of large compound libraries. Therefore, in silico approaches could be very beneficial to indicate hazards early in the drug development pipeline. By combining multiple endpoints, we derived the largest so far published dataset on mitochondrial toxicity. A thorough data analysis shows that molecules causing mitochondrial toxicity can be distinguished by physicochemical properties. Finally, the combination of machine learning and structural alerts highlights the suitability for in silico risk assessment of mitochondrial toxicity.
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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 Socratic graph.