title: Prediction of IC50 values for ACAT inhibitors from molecular structure.
abstract: A quantitative structure-activity study is performed on several series of compounds derived from N-chlorosulfonyl isocyanate to develop models that relate their structures to IC50 activity for inhibition of acyl-CoA:cholesterol O-acyltransferase (ACAT). Numerical descriptors are used to encode topological, electronic, and geometric information from the molecular structures of the inhibitors. A data set of 157 compounds showing triglyceride- and cholesterol-lowering activity is used to develop successful linear regression models and nonlinear computational neural network models. The models are validated using an external prediction set.
authors.affiliation: Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, USA.
authors.forename: S J
authors.initials: SJ
authors.lastname: Patankar
authors.forename: P C
authors.initials: PC
authors.lastname: Jurs
mesh_terms: D004791:Enzyme Inhibitors / Q000737:chemistry* / Q000494:pharmacology*; D015394:Molecular Structure; D002785:Sterol O-Acyltransferase / Q000037:antagonists & inhibitors*; D013329:Structure-Activity Relationship
chemical_list: D004791:Enzyme Inhibitors; D002785:Sterol O-Acyltransferase