AI-Driven Multi-Omics Integration for Enhanced Drug Discovery Pipelines

Integration of multi-omics data-genomics, transcriptomics, proteomics, and metabolomics-has become a crucial approach in the recent era for accelerating drug discovery. Artificial Intelligence synthesizes this multifarious data to provide novel insights into complex biological mechanisms underlying disease pathophysiology. In this study, AI-driven machine learning algorithms were utilized to integrate publicly available datasets from resources such as The Cancer Genome Atlas and Gene Expression Omnibus. Our method is using deep learning approaches to identify new biomarkers and therapeutic targets by detecting complex patterns and their interactions in multiple omics layers. By applying this integrated framework on real-world datasets, we successfully identified several candidate compounds with potential efficacy against specific cancer subtypes, demonstrating enhanced predictive accuracy compared to traditional single-omics approaches. Moreover, our approach simplifies the pipeline of drug discovery by saving time and costs of experimental validations. These results point out the potential impact of AI-driven multi-omics integration on the discovery of disease molecular mechanisms and acceleration of targeted therapy development. This work emphasizes the importance of an interdisciplinary approach using cutting-edge computational techniques to fully exploit the potentiality of multi-omics data in pharmaceutical research.