ArticleFrontiers in pharmacology2020
Dual Transcriptomic and Molecular Machine Learning Predicts all Major Clinical Forms of Drug Cardiotoxicity.
Article in Frontiers in pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 31 citations in OpenAlex.
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Review
- Predicting cardiotoxicity in drug development: A deep learning approach.Journal of pharmaceutical analysis · 2025Article
- A Closed Circulation Langendorff Heart Perfusion Method for Cardiac Drug Screening.Physiological research · 2024Article
- Multiscale mapping of transcriptomic signatures for cardiotoxic drugs.Nature communications · 2024Article
- Review of machine learning and deep learning models for toxicity prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- Artificial intelligence in pharmacology research and practice.Clinical and translational science · 2023Review
- Shared Genetic Risk Factors Between Cancer and Cardiovascular Diseases.Frontiers in cardiovascular medicine · 2022Article
- Multimodality Advanced Cardiovascular and Molecular Imaging for Early Detection and Monitoring of Cancer Therapy-Associated Cardiotoxicity and the Role of Artificial Intelligence and Big Data.Frontiers in cardiovascular medicine · 2022Review
- Artificial Intelligence in Drug Safety and Metabolism.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Toward a broader view of mechanisms of drug cardiotoxicity.Cell reports. Medicine · 2021Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Computational methods can increase productivity of drug discovery pipelines, through overcoming challenges such as cardiotoxicity identification. We demonstrate prediction and preservation of cardiotoxic relationships for six drug-induced cardiotoxicity types using a machine learning approach on a large collected and curated dataset of transcriptional and molecular profiles (1,131 drugs, 35% with known cardiotoxicities, and 9,933 samples). The algorithm generality is demonstrated through validation in an independent drug dataset, in addition to cross-validation. The best prediction attains an average accuracy of 79% in area under the curve (AUC) for safe versus risky drugs, across all six cardiotoxicity types on validation and 66% on the unseen set of drugs. Individual cardiotoxicities for specific drug types are also predicted with high accuracy, including cardiac disorder signs and symptoms for a previously unseen set of anti-inflammatory agents (AUC = 80%) and heart failures for an unseen set of anti-neoplastic agents (AUC = 76%). Besides, independent testing on transcriptional data from the Drug Toxicity Signature Generation Center (DToxS) produces similar results in terms of accuracy and shows an average AUC of 72% for previously seen drugs and 60% for unseen respectively. Given the ubiquitous manifestation of multiple drug adverse effects in every human organ, the methodology is expected to be applicable to additional tissue-specific side effects beyond cardiotoxicity.
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Registered trials
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