Authors
Zhang, S., Ohlan, R., Mottaqi, M., Xie, L.
Abstract
Generative artificial intelligence (AI) has emerged as a powerful framework for drug discovery, yet most current approaches follow one-drug-one-gene target-based paradigms that struggle to capture the complexity and heterogeneity of chronic and systemic diseases. Omics-driven systems pharmacology provides a promising strategy to overcome these limitations, but generative AI tools specifically designed for systems pharmacology-oriented drug design remain scarce. To address this gap, we introduce GEM-GPT, a transcriptomics-based molecule generation framework that designs personalized therapeutic compounds capable of reverting cell type-specific disease states back to a healthy phenotype. GEM-GPT employs a biology-inspired deep fusion architecture that couples a single-cell RNA-sequencing (scRNA-seq) foundation model with a molecular GPT model, enabling the modeling of cell type-specific chemical-gene interactions during molecule generation. This integration allows GEM-GPT to outperform state-of-the-art baselines, generate distinct molecules for different cell types, and generalize robustly to previously unseen cell types. We further demonstrate the utility of GEM-GPT through a case study in personalized drug discovery for opioid use disorder (OUD). In this application, GEM-GPT successfully identifies both therapeutic compounds possessing distinct chemotypes and existing FDA-approved drugs predicted to modulate cell type-specific OUD disease phenotypes in individual patients. Together, these results establish GEM-GPT as an advance in AI-driven systems pharmacology by bridging single-cell omics and molecular generation to support personalized, systems-aware therapeutic design.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 23 Jul 2026.
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