A Novel Integrative Framework for Depression: Combining Network Pharmacology, Artificial Intelligence, and Multi-Omics with a Focus on the Microbiota–Gut–Brain Axis

Jan 30, 2026Current issues in molecular biology

A new combined approach to depression using drug networks, artificial intelligence, and multiple biological data focused on the gut-brain connection

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Abstract

An integrative framework incorporating network pharmacology, artificial intelligence, and multi-omics technologies could enhance the understanding and management of Major Depressive Disorder (MDD).

A holistic perspective is proposed to address the complex pathophysiology of MDD beyond conventional treatment methods.
AI-powered integration of multi-omics data may facilitate the construction of predictive network models that uncover fundamental mechanisms of the disease.
The review examines the microbiota-gut-brain axis, highlighting its role in neuroimmune alterations and metabolic dysfunction associated with MDD.
The proposed translational roadmap includes AI-assisted biomarker discovery and computational drug repurposing for personalized treatment approaches.
Challenges related to data heterogeneity, model interpretability, and clinical implementation may hinder progress in precision psychiatry for MDD.

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Major Depressive Disorder (MDD) poses a significant global health burden, characterized by a complex and heterogeneous pathophysiology insufficiently targeted by conventional single-treatment approaches. This review presents an integrative framework incorporating network pharmacology, artificial intelligence (AI), and multi-omics technologies to advance a systems-level understanding and management of MDD. Its central contribution lies in moving beyond reductionist methods by embracing a holistic perspective that accounts for dynamic interactions within biological networks. The primary objective is to demonstrate how AI-powered integration of multi-omics data-spanning genomics, proteomics, and metabolomics-can enable the construction of predictive network models. These models are designed to uncover fundamental disease mechanisms, identify clinically relevant biotypes, and reveal novel therapeutic targets tailored to specific pathological contexts. Methodologically, the review examines the microbiota-gut-brain (MGB) axis as an illustrative case study, detailing its pathogenic roles through neuroimmune alterations, metabolic dysfunction, and disrupted neuro-plasticity. Furthermore, we propose a translational roadmap that includes AI-assisted biomarker discovery, computational drug repurposing, and patient-specific "digital twin" models to advance precision psychiatry. Our analysis confirms that this integrated framework offers a coherent route toward mechanism-based personalized therapies and helps bridge the gap between computational biology and clinical practice. Nevertheless, important challenges remain, particularly pertaining to data heterogeneity, model interpretability, and clinical implementation. In conclusion, we stress that future success will require integrating prospective longitudinal multi-omics cohorts, high-resolution digital phenotyping, and ethically aligned, explainable AI (XAI) systems. These concerted efforts are essential to realize the full potential of precision psychiatry for MDD.

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A Novel Integrative Framework for Depression: Combining Network Pharmacology, Artificial Intelligence, and Multi-Omics with a Focus on the Microbiota–Gut–Brain Axis

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