title: Simplified molecular input line entry system-based optimal descriptors: QSAR modelling for voltage-gated potassium channel subunit Kv7.2.
abstract: CORAL software has been used to build quantitative structure-activity relationships (QSARs) for the prediction of binding affinities (pEC50, i.e., minus decimal logarithm of the 50% effective concentration) of 35 potent inhibitors towards the voltage-gated potassium channel subunit Kv7.2. The pEC50 has been modelled using eight random splits, with the following representations of the molecular structure: (i) hydrogen-suppressed graph (HSG); (ii) simplified molecular input line entry system (SMILES); (iii) graph atomic orbitals (GAOs) and (iv) hybrid representation, which is HSG together with SMILES. These models have been examined using three methods, the classic scheme, balance correlation, and balance correlation with ideal slope. The QSAR model based on single optimal descriptors using SMILES provided the best accuracy for the prediction of the pEC50. The robustness of these models has been checked using parameters such as rm(2), r(*)m(2), [Formula: see text], and using a randomization technique. The best QSAR model based on single optimal descriptors has been applied to study the in vitro structure-activity relationships of pyrazolo[1,5-a]pyrimidin-7(4H)-one derivatives as Kv7.2 modulators. The pEC50 is found to be significantly increased by the incorporation of -OH, -NO2 or -Br groups in place of one -F, whereas -NH2 has a negative effect on the pEC50 values.
authors.affiliation: a Department of Chemistry, Institute of Technical Education and Research (ITER) , Siksha 'O' Anusandhan University , Bhubaneswar , Odisha - 751030.
authors.forename: P Ganga Raju
authors.initials: PG
authors.lastname: Achary
mesh_terms: D051658:KCNQ2 Potassium Channel / Q000737:chemistry*; D008958:Models, Molecular; D015394:Molecular Structure; D009010:Monte Carlo Method; D021281:Quantitative Structure-Activity Relationship; D012984:Software
chemical_list: D051658:KCNQ2 Potassium Channel