The data of MLT project

To address the challenges and harness the immense therapeutic potential of dual-targeting interventions for aging and AD, we developed a novel artificial intelligence-guided method termed the Pathway and Transcriptome-Driven Drug Efficacy Predictor (PTD-DEP). This model was specifically designed for the systematic identification and optimization of small-molecule candidates capable of targeting shared pathological pathways underlying aging and AD. Leveraging PTD-DEP, we identified melatonin (MLT), an endogenous hormone commonly used clinically as an anti-insomnia agent, as a promising candidate exhibiting dual anti-aging and anti-AD therapeutic potential, which we subsequently validated through comprehensive in vitro and in vivo studies. Mechanistically, guided by Proteolysis Targeting Chimera (PROTAC) technology combined with CB-Dock2 computational prediction, we uncovered p300 as a critical molecular target of MLT. Further integrative analyses employing CUT&Tag, immunoprecipitation-mass spectrometry (IP-MS), single-cell RNA sequencing (scRNA-seq), and spatial transcriptomics, complemented by rigorous pharmacological validations, revealed that MLT specifically targets the P300/SP1 transcriptional complex localized within super-enhancer regions. This targeted interaction potently drives transcriptional activation of BMAL1, a pivotal regulator of circadian rhythm 26. Notably, the therapeutic efficacy of MLT was robustly confirmed across preclinical models of both AD and cellular senescence, underscoring its unique polypharmacological capacity to concurrently mitigate aging hallmarks and AD pathology.