Rogawski MA, Porter RJ (1990) Antiepileptic drugs: pharmacological mechanisms and clinical efficacy with consideration of promising developmental stage compounds. Marson AG, Kadir ZA, Hutton JL, Chadwick DW (1997) The new antiepileptic drugs: a systematic review of their efficacy and tolerability. Litt B, Echauz J (2002) Prediction of epileptic seizures. Lancet 16:689–701įisher RS, van Emde BW, Blume W, Elger C, Genton P, Lee P, Engel J Jr (2005) Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Thijs RD, Surges R, O’Brien TJ, Sander JW (2019) Epilepsy in adults. Miller JM, Kustra RP, Vuong A, Hammer AE, Messenheimer JA (2008) Depressive symptoms in epilepsy: prevalence, impact, aetiology, biological correlates and effect of treatment with antiepileptic drugs. Brain 126:753–769ĭuncan JS, Sander JW, Sisodiya SM, Walker MC (2006) Adult epilepsy. Taylor I, Scheffer IE, Berkovic SF (2003) Occipital epilepsies: identification of specific and newly recognized syndromes. McCormick DA, Contreras D (2001) On the cellular and network bases of epileptic seizures.

Marini C, Giardino M (2022) Novel treatments in epilepsy guided by genetic diagnosis. Based on the results, all the designed molecules indicate the presence of high drug-likeness. The computation of physicochemical descriptors was conducted in order to predict ADME parameters, pharmacokinetic properties, the drug-like nature and medicinal chemistry friendliness, with the aim of supporting drug discovery. This emphasized excellent correlation with QSAR modeling results.

Molecular docking studies were referred to when making the final assessment of the designed inhibitors. The discovered molecular fragments utilized for the preparation of the computer-aided design of the new compounds were thought to have led to the increase and decrease of the examined activity. Along with the robustness of the developed QSAR model, the used statistical methods yielded an excellent predictability potential. The developed QSAR model was validated with the use of various statistical parameters, such as the correlation coefficient, cross-validated correlation coefficient, standard error of estimation, mean absolute error, root-mean-square error R m 2, MAE-based metrics, the Fischer ratio, as well as the correlation ideality index. The molecular descriptors involve the local molecular graph invariants and the SMILES notation for the molecules whose antiMES activity is active against maximal electroshock seizure (MES). The paper deals with quantitative structure–activity relationship (QSAR) modeling-based Monte Carlo optimization.

Manifestation journal template