Computational Drug Repurposing for Alzheimer’s Disease via Sheaf Theoretic Population-Scale Analysis of snRNA-Seq Data
Year: 2026
Type: article
Abstract: Single-cell and single-nucleus RNA sequencing are used to reveal heterogeneity in cells, showing a growing potential for precision and personalized medicine. Nevertheless, sustainable drug discovery must be based on a population-level understanding of molecular mechanisms, which calls for a population-scale analysis of this data. This work introduces a sequential target-drug selection model for drug repurposing against Alzheimer's Disease (AD) targets inferred from snRNA-seq data of AD progression- involving hundreds of thousands of nuclei from multipatient and multiregional studies. We utilize Persistent Sheaf Laplacians (PSL) to facilitate a Protein-Protein Interaction (PPI) analysis inferred from disease related differential gene expression (DEG). We then use an ensemble of machine learning models to predict repurpose-able compounds. We screen the efficacy of different small compounds and further examine their central nervous system relevant ADMET properties, resulting in a list of potential molecular targets as well as pharmaceutical lead candidates for AD treatment.
Source: Journal of Medicinal Chemistry
Authors: Sean Cottrell, Seungmin Yoon, Xiaoqi Wei, Alex Dickson, Guo-Wei Wei
Institution: Michigan State University
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
Awards: AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies, G2333693723, Collaborative Research: Integrating Algebraic Topology, Graph Theory, and Multiscale Analysis for Learning Complex and Diverse Datasets, G6065440738, III: Medium: De Rham-Hodge theory modeling and learning of biomolecular data, G744120195, G754965114, G9000294604, G9000342653
OpenAlex ID: W7129078146