# AI-driven identification of nutrition-modulated biomarkers and drug targets for cardiovascular therapeutic mechanisms

## TL;DR

This paper explores how AI can help identify nutrition-related biomarkers and drug targets for cardiovascular diseases, offering new therapeutic strategies.

## Contribution

The paper introduces AI-based strategies to integrate nutrition into cardiovascular drug discovery, focusing on modifiable molecular signals.

## Key findings

- Nutrients and metabolites regulate key cardiovascular pathways overlapping with drug targets.
- AI techniques like machine learning and multi-omics integration can prioritize biomarkers and therapeutic targets.
- AI-enabled nutritional pharmacology offers translational potential for precision cardiovascular therapeutics.

## Abstract

Cardiovascular diseases (CVD) remain the leading cause of disease burden and mortality worldwide. Despite significant progress in drug treatment, this situation indicates that persistent residual risks still exist even after all feasible risk control measures have been implemented. Nutrition is increasingly recognized as an important modulator of cardiovascular biology; however, its integration into pharmacological frameworks for biomarker discovery and drug target identification has remained limited, largely due to insufficient mechanistic resolution and analytical complexity. Recent progress in high-throughput multi-omics technologies has revealed that nutrients and nutrient-derived metabolites directly regulate key pathways involved in lipid metabolism, inflammation, and mitochondrial function, many of which overlap with established or emerging cardiovascular drug targets. In parallel, artificial intelligence (AI) has emerged as a powerful discovery engine capable of integrating high-dimensional nutritional, molecular, and clinical data to prioritize biomarkers and uncover therapeutically actionable targets. In this mini-review, unlike previous studies that focused on dietary patterns and behavioral recommendations, we have summarized the current evidence regarding the drugable pathways for nutritional regulation in cardiovascular diseases, and have particularly highlighted the strategies based on artificial intelligence - including machine learning, network pharmacology, and multi-omics integration - for identifying biomarkers and elucidating therapeutic mechanisms. We further discuss the translational implications of AI-enabled nutritional pharmacology for precision cardiovascular therapeutics. By reframing nutrition as a source of modifiable molecular signals rather than a lifestyle exposure, this review provides a mechanistic framework for harnessing AI to advance biomarker discovery and drug target identification in cardiovascular disease.