Deep learning identifies TP-41 for methylglyoxal scavenging in Alzheimer's treatment.

ABSTRACT: Rationale: Increased levels of advanced glycation end products (AGEs) have been observed in the brain tissues of patients with Alzheimer's disease (AD). Methylglyoxal (MGO) is a potent precursor of AGEs. To date, there have been no reports of utilizing deep learning (DL) technologies to target MGO scavengers for the development of AD therapeutics. Therefore, DL-driven approaches may play a crucial role in identifying potential MGO scavengers and candidates for Alzheimer's treatment. Methods: We developed "DeepMGO," a novel DL-based MGO scavenging activity prediction model, trained on 2,262 MGO scavenging activity assays from 660 compounds. Using this approach, we identified and validated TP-41 as a potential MGO scavenger in a mouse model of memory impairment. Results:

SUBMITTER: Park A 

PROVIDER: S-EPMC12679084 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature