AI-based prediction and validation in zebrafish model elucidates potential bioactivity of novel thiazolone derivative

The multifactorial etiology of age-related complex diseases, including Alzheimer's disease (AD), cancer, and type 2 diabetes mellitus (T2DM), challenges the efficacy of single-target therapeutics. The development of multi-target-directed ligands (MTDLs) represents a promising strategy to address these overlapping pathological networks. In this study, we employed a high-throughput phenotypic screening platform integrated with artificial intelligence (AI) to evaluate the pharmacological potential of a novel thiazolone derivative, Compound M. Consensus clustering analysis of zebrafish behavioral and physiological phenotypes against 103 clinical drugs predicted that Compound M possesses anti-AD activity at low concentrations and antitumor/antihyperglycemic effects at higher concentrations. Subsequent in vivo and in vitro validations confirmed these pleiotropic activities. In a zebrafish xenograft model, Compound M significantly inhibited Jurkat tumor cell proliferation (P < 0.001), exhibiting an in vitro cell viability IC50 of 10.7 μM. Furthermore, the compound demonstrated potent hypoglycemic effects in a high-fat diet-induced zebrafish model, comparable to the standard drug metformin. In an AlCl 3 -induced AD zebrafish model, Compound M significantly reduced acetylcholinesterase (AChE) activity by 38% (P < 0.001). In silico ADMET profiling predicted favorable intestinal absorption but limited blood-brain barrier (BBB) permeability and a potential risk for cardiac toxicity (hERG). Molecular docking studies revealed that Compound M engages the active sites of AChE and BACE1 primarily through hydrophobic interactions, acting as a partial-site binder. These findings establish Compound M as a promising lead scaffold for MTDL development, while highlighting specific structural optimization strategies—such as scaffold extension and physicochemical tuning—to enhance its CNS penetration and safety profile.