# Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery

## TL;DR

This review explores how deep learning helps combine different biological data to discover new drug targets, improving precision medicine and reducing pharmaceutical costs.

## Contribution

The paper introduces a systematic review of deep learning methods for multi-omics integration in drug target discovery.

## Key findings

- DL-driven multi-omics integration can identify novel disease drivers and therapeutic targets.
- Challenges include data sparsity, model interpretability, and target validation.
- Emerging AI techniques like Generative AI and XAI offer opportunities to overcome these challenges.

## Abstract

The identification of novel drug targets is essential for developing effective therapies and reducing the substantial costs and high failure rates in pharmaceutical research. Traditional analytical methods focusing on a single biological layer often fail to capture the systemic complexity of human diseases. This review summarizes advancements in computational frameworks that integrate diverse biological data, including genes, proteins, and metabolites, to facilitate drug target discovery. We examine how these methodologies identify disease drivers, predict genetic interactions, and prioritize potential therapeutic candidates. Furthermore, this work evaluates critical challenges such as data sparsity, the limited interpretability of models, and the necessity of assessing the chemical and clinical feasibility of predicted targets. By addressing data inconsistencies and establishing transparent, comprehensive evaluation frameworks, these integrated approaches offer the potential to advance precision medicine and enhance the delivery of effective treatments to patients.

Precise drug target discovery is pivotal to mitigating the escalating costs and high attrition rates that characterize pharmaceutical research and development. Given that traditional single-omics methods often fail to elucidate the systemic complexity of human diseases, deep learning (DL)-enabled multi-omics integration has emerged as a transformative frontier. This review systematically summarizes the advancements in DL-driven multi-omics integration for drug target discovery. First, the multi-omics data foundation and integration strategies are delineated, followed by an exploration of the DL architectures utilized for processing such data. Subsequently, the efficacy of DL-driven multi-omics integration is examined regarding the identification of novel disease drivers, prediction of synthetic lethality interactions, and prioritization of therapeutic targets. Finally, addressing persistent challenges related to data sparsity, model interpretability, and target druggability and validation hurdles, emerging opportunities driven by Generative AI, Large Multimodal Models (LMMs), Explainable AI (XAI), and multidimensional feasibility assessment frameworks are discussed in the context of advancing precision medicine.