In Silico ADMET Profiling and Drug-Likeness Evaluation of Novel Thiopyrano[2,3-d]thiazole Derivatives as Potential Anticonvulsants

The development of novel antiepileptic agents requires early identification of pharmacokinetic limitations to mitigate risks at later stages. This study aimed to perform in silico profiling of a library containing 448 novel 2H,5H-chromeno[4’,3’:4,5]thiopyrano[2,3-d]thiazol-2-one derivatives to select lead compounds with an optimal balance of safety and efficacy. The study was conducted using the ADMET-AI platform, based on a graph neural network, to predict physicochemical, pharmacokinetic, and toxicological properties. The methodology involved calculating drug-likeness descriptors for primary screening and a comparative statistical analysis of the top 20 selected structures against 16 approved antiepileptic drugs and four reference compounds. Based on drug-likeness descriptors and predicted ADMET (absorption, distribution, metabolism, excretion, toxicity) related parameters, 20 structures were prioritized for further analysis. Their predicted profiles suggested high intestinal absorption and blood–brain barrier (BBB) permeability, which may be relevant for central nervous system (CNS) directed agents. In comparison with the reference thiazolidinones, the prioritized compounds showed comparatively more favorable predicted mutagenicity and carcinogenicity profiles. Elevated predicted risks of hepatotoxicity and cardiotoxicity were observed for several structures, indicating the need for further structural optimization. The results suggest that the thiopyranothiazolidinone scaffold merits further anticonvulsant-oriented investigation at the stage of early compound prioritization. Experimental validation will be required to confirm the actual pharmacokinetic, toxicological, and anticonvulsant properties of the prioritized compounds.