Структурно-фрагментний аналіз діючих речовин протиепілептичних препаратів групи N03A на фармацевтичному ринку України та їх фармакофорні особливості

Epilepsy affects approximately 50 million people globally, with one-third of patients remaining resistant to available therapies, emphasizing the need for new and safer anticonvulsants. Although fragment-based and in silico approaches are effective for drug discovery, a unified structural analysis of antiepileptic APIs on the Ukrainian market remains unexplored. The aim of the study. To analyze 16 antiepileptic APIs registered in Ukraine using fragment-based methods to identify shared pharmacophoric features, structural similarities, and correlations between structural fragments and ADME properties (including drug-likeness patterns for structure-property insights) as a basis for rational anticonvulsant design. Materials and methods. Data were collected from the State Register of Medicinal Products of Ukraine and Compendium (June 2025) using ATC code N03A. Literature review used PubMed, PubChem, DrugBank, Scopus, Elicit, and ResearchRabbit. Structural analysis was performed using Python libraries. Results. The study classified 16 active pharmaceutical ingredients (APIs) into structural clusters (e.g., barbiturates, dibenzazepines, amino acid derivatives) based on Tanimoto similarity coefficients and ECFP4 molecular fingerprints. Commonly identified fragments included carbonyl, amino, amide, carboxyl groups, and aromatic rings. ADME profiling revealed consistent relationships between structural features and physicochemical properties: high lipophilicity in benzodiazepines and good absorption characteristics in gabapentinoids. This analysis was performed to identify structure-dependent ADME patterns, providing a basis for fragment-based design of novel anticonvulsants. Conclusions. Despite chemical diversity, the analyzed APIs exhibit shared spatial pharmacophore arrangements with recurring groups supporting activity at NaV, CaV, GABA-A, SV2A, and GABA-T. ADME profiling and structure–property correlations provide a basis for pharmacophore fragment modelling and CNS-oriented fragment-library design to enable rational discovery. Future design should leverage the identified pharmacophoric fragments to build multitarget molecules within a CNS ADME window