Deep learning-based drug screening for the discovery of potential therapeutic agents for Alzheimer's disease.

ABSTRACT: Alzheimer's disease (AD) is gradually increasing in prevalence and the complexity of its pathogenesis has led to a lengthy process of developing therapeutic drugs with limited success. Faced with this challenge, we proposed using a state-of-the-art drug screening algorithm to identify potential therapeutic compounds for AD from traditional Chinese medicine formulas with strong empirical support. We developed four deep neural network (DNN) models for AD drugs screening at the disease and target levels. The AD model was trained with compounds labeled for AD activity to predict active compounds at the disease level, while the acetylcholinesterase (AChE), monoamine oxidase-A (MAO-A), and 5-hydroxytryptamine 6 (5-HT₆) models were trained for specific AD targets. All four models perfo

SUBMITTER: Wu T 

PROVIDER: S-EPMC11755336 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature