# Computational Drug Repurposing for Alzheimer’s Disease via Sheaf Theoretic Population-Scale Analysis of snRNA-Seq Data

## TL;DR

This paper uses advanced computational methods to find new drugs for Alzheimer’s by analyzing large-scale brain cell data.

## Contribution

A novel sheaf-theoretic approach is introduced for population-scale analysis of snRNA-seq data to repurpose drugs for Alzheimer’s.

## Key findings

- Persistent Sheaf Laplacians were used to analyze PPI networks from AD-related gene expression data.
- Machine learning models identified potential drug candidates with favorable CNS-related ADMET properties.
- The study generated a list of molecular targets and pharmaceutical leads for Alzheimer’s treatment.

## 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.