A computational medicine framework integrating multi-omics, systems biology, and artificial neural networks for Alzheimer’s disease therapeutic discovery

Journal article

The translation of genetic findings from genome-wide association studies into actionable therapeutics persists as a critical challenge in Alzheimer’s disease (AD) research. Here, we present PI4AD, a computational medicine framework that integrates multi-omics data, systems biology, and artificial neural networks for therapeutic discovery. This framework leverages multi-omic and network evidence to deliver three core functionalities: clinical target prioritisation; self-organising prioritisation map construction, distinguishing AD-specific targets from those linked to neuropsychiatric disorders; and pathway crosstalk-informed therapeutic discovery. PI4AD successfully recovers clinically validated targets like APP and ESR1, confirming its prioritisation efficacy. Its artificial neural network component identifies disease-specific molecular signatures, while pathway crosstalk analysis reveals critical nodal genes (e.g., HRAS and MAPK1), drug repurposing candidates, and clinically relevant network modules. By validating targets, elucidating disease-specific therapeutic potentials, and exploring crosstalk mechanisms, PI4AD bridges genetic insights with pathway-level biology, establishing a systems genetics foundation for rational therapeutic development. Importantly, its emphasis on Ras-centred pathways—implicated in synaptic dysfunction and neuroinflammation—provides a strategy to disrupt AD progression, complementing conventional amyloid/tau-focused paradigms, with the future potential to redefine treatment strategies in conjunction with mRNA therapeutics and thereby advance translational medicine in neurodegeneration. The PI4AD portal is accessible at http://www.genetictargets.com/PI4AD.

Authors: Yang, Y; Diao, Y; Jiang, L; Li, F; Chen, L

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Acta Pharmaceutica Sinica B
• Volume: 15
• Issue: 9
• Pages: 4411-4426
• Publication date: 2025-07-16
• Acceptance date: 2025-05-28
• DOI: 10.1016/j.apsb.2025.07.018
• EISSN: 2211-3843
• ISSN: 2211-3835
• Language: English
• Keywords: brain disorders; aging; machine learning and artificial intelligence; alzheimer's disease; bioengineering; dementia; acquired cognitive impairment; human genome; networking and information technology r&d (nitrd); alzheimer's disease including alzheimer's disease related dementias (ad/adrd); precision medicine; neurological; genetics; neurodegenerative; biotechnology; neurosciences