Multi-Omics Advances in Major Depressive Disorder for Molecular Insights, Biomarker Discovery, and Therapeutic Development

Major depressive disorder (MDD) affects hundreds of millions worldwide and remains a major unmet clinical need because conventional monoaminergic agents achieve remission in fewer than half of patients and leave a substantial treatment-resistant subgroup. To address this gap, integrative multi-omics approaches, combined with computational systems biology, are being used to dissect its multifactorial etiology. Here, we review key findings from large Genome-wide association studies (GWAS), single-cell and spatial transcriptomics, proteomics, and metabolomics that converge on disrupted neuroplasticity, immune-inflammatory signaling, HPA-axis dysregulation, mitochondrial metabolism, and gut-brain interactions. We describe how AI-driven network modeling and structure-based drug design (SBDD) are translating multi-omics signals into candidate biomarkers and mechanism-based therapeutics, for example, N-methyl-D-aspartate/glutamatergic modulators, kappa opioid antagonists, anti-inflammatory agents, and epigenetic modulators. We highlighted the clinical implications, specifically molecularly stratified biomarkers for patient selection, trial enrichment, and structure-guided optimization of rapid-acting antidepressants and microbiome-based interventions. Finally, we discuss the limitations and immediate translational priorities that emphasize the trajectory from multi-omics discovery to precision psychiatry for MDD.