Exploring Plausible Therapeutic Targets for Alzheimer's Disease using Multi-omics Approach, Machine Learning and Docking

The progressive deterioration of neurons leads to Alzheimer's disease (AD), and developing a drug for this disorder is challenging. Substantial gene/transcriptome variability from multiple cell types leads to downstream pathophysiologic consequences that represent the heterogeneity of this disease. Identifying potential biomarkers for promising therapeutics is strenuous due to the fact that the transcriptome, epigenetic, or proteome changes detected in patients are not clear whether they are the cause or consequence of the disease, which eventually makes the drug discovery efforts intricate. The advancement in scRNA-sequencing technologies helps to identify cell type-specific biomarkers that may guide the selection of the pathways and related targets specific to different stages of the disease progression. This review is focussed on the analysis of multi-omics data from various perspectives (genomic and transcriptomic variants, and single-cell expression), which provide insights to identify plausible molecular targets to combat this complex disease. Further, we briefly outlined the developments in machine learning techniques to prioritize the risk-associated genes, predict probable mutations and identify promising drug candidates from natural products.

Automated summary

The development of drugs for Alzheimer's disease is challenging due to the progressive deterioration of neurons and the heterogeneity of the disease. Single-cell RNA sequencing technologies help identify cell type-specific biomarkers for selecting therapeutic targets. This review also highlights the use of machine learning techniques in Alzheimer's disease research.