ArticleExploration of medicine2025
AI-driven drug discovery and repurposing using multi-omics for myocardial infarction and heart failure.
Article in Exploration of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Molecular Stability as a Translational Gate: A Structured Framework for Target Validation in Genetic Cardiomyopathy.Cureus · 2026Review
- Therapeutic rewiring of ceRNA networks: a computational pipeline for drug repurposing in acute kidney injury via circRNA-miRNA-mRNA axis disruption.Human genomics · 2026Review
- AI-driven identification of nutrition-modulated biomarkers and drug targets for cardiovascular therapeutic mechanisms.Frontiers in pharmacology · 2026Review
- Bridging science and hope: the evolving story of gene therapy for neuromuscular diseases.Frontiers in cell and developmental biology · 2026Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Cardiovascular diseases (CVDs) are the leading causes of morbidity and mortality worldwide. Yet, drug discovery for these conditions faces significant challenges due to the complexity and heterogeneity of their underlying pathology. Recently, artificial intelligence (AI) techniques-particularly explainable AI (XAI)-have emerged as powerful multi-omics data analyzing tools to unravel pathological mechanisms and novel therapeutic targets. However, the application of XAI in cardiovascular drug discovery remains in its infancy. This review discusses the potential for the integration of AI with multi-omics data to identify novel therapeutic targets and repurpose existing drugs for myocardial infarction (MI) and heart failure (HF). This review highlights the current gap in leveraging XAI for CVDs and discusses key challenges such as data heterogeneity, model interpretability, and translational validation. This review also describes emerging approaches, including combining AI with mechanistic models, that aim to enhance the biological relevance of AI predictions. By utilizing genomic, transcriptomic, epigenomic, proteomic, and metabolomic datasets, AI-driven methods can uncover new biomarkers and predict drug responses with greater precision. The application of AI in analyzing large-scale clinical and molecular data offers significant promise in accelerating drug discovery, refining therapeutic strategies, and improving outcomes for patients with CVDs. This review highlights recent advancements, challenges, and future directions for AI-guided drug discovery in the context of MI and HF.
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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.