Work

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

Year: 2025

Type: article

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: DeepMGO demonstrated robust predictive performance and identified novel compounds with high MGO scavenging activity.TP-41 ameliorated depression symptoms and memory deficits in mouse models.Conclusions: Using DeepMGO, we identified TP-41 as a potential therapeutic agent for AD.

Source: Theranostics

DOI: 10.7150/thno.111550