J Cardiovasc Pharmacol. 2026 Feb 3. doi: 10.1097/FJC.0000000000001803. Online ahead of print.
ABSTRACT
Cardiovascular diseases remain the leading cause of global morbidity and mortality, highlighting the urgent need for more efficient, precise, and cost-effective drug development strategies. Traditional drug discovery pipelines face persistent challenges, including elevated expenses, prolonged timelines, and high attrition rates, particularly with the complex pathophysiology of cardiovascular conditions. Artificial intelligence (AI) has emerged as a transformative force capable of addressing these barriers across all stages of cardiovascular drug development. This review explores the integration of AI in target identification, compound screening, drug design, pharmacokinetic and toxicity prediction, and clinical trial optimization. We highlight state-of-the-art AI tools such as large language models (e.g., BioGPT, Geneformer), generative frameworks (e.g., Generative Tensorial Reinforcement Learning (GENTRL), Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs)), and neural ordinary differential equations, illustrating their ability to accelerate drug discovery, personalize therapy, and improve clinical success rates. In the context of clinical trials, platforms such as Trial Pathfinder have been employed to optimize patient recruitment and improve generalizability. Despite these advancements, several challenges persist, particularly those related to data quality, population representativeness, interpretability, and regulatory oversight. Future directions involving the integration of AI with quantum computing, blockchain technology, and precision medicine offer additional opportunities to advance the field. Collectively, these innovations mark a paradigm shift toward faster, safer, and more personalized cardiovascular drug development.
PMID:41632854 | DOI:10.1097/FJC.0000000000001803