QSAR studies on BK channel activators

• Published in: Bioorganic & medicinal chemistry Vol. 17; no. 1; pp. 319 - 325
• Main Authors: Coi, Alessio, Fiamingo, Francesca Lidia, Livi, Oreste, Calderone, Vincenzo, Martelli, Alma, Massarelli, Ilaria, Bianucci, Anna Maria
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 2009; Elsevier
• Subjects: Artificial Intelligence; Biological and medical sciences; BK channel; CODESSA; Humans; Large-Conductance Calcium-Activated Potassium Channels - agonists; Medical sciences; Miscellaneous; Pharmacology. Drug treatments; QSAR; Quantitative Structure-Activity Relationship; Software; MM; BK channel; MLR; QSAR; CODESSA; TR; TS; BK potassium channel; Agonist; Structure activity relation; Prediction; Affinity; Ionic channel; Potassium Ions
• ISSN: 0968-0896; 1464-3391
• DOI: 10.1016/j.bmc.2008.10.068

QSAR studies were developed on the basis of a dataset comprising BK channel activators previously synthesized and biologically assayed in our laboratory, in order to obtain highly accurate models enabling prediction of affinity toward the channel for New Chemical Entities (NCEs). Many molecular descriptors were computed by the CODESSA software. They were initially exploited in order to rationally split the available dataset into training and test set pairs, which supplied the basis for the development of QSAR models. Models were subjected to rigorous validation analysis based on the estimate of several statistical parameters, for the seek of the most accurate and simplest model enabling prediction of BK channel affinity.