title: Structure-activity relationship study of trifluoromethylketone inhibitors of insect juvenile hormone esterase: comparison of several classification methods.
abstract: Juvenile hormone esterase (JHE) plays a key role in the development and metamorphosis of holometabolous insects. Its inhibitors could possibly be targeted for insect control. Conversely, JHE may also be involved in endocrine disruption by xenobiotics, resulting in detrimental effects in beneficial insects. There is therefore a need to know the structural characteristics of the molecules able to monitor JHE activity, and to develop SAR and QSAR studies to estimate their effectiveness. For a large diverse population of 181 trifluoromethylketones (TFKs) - the most potent JHE inhibitors known to date - we recently proposed a binary classification (active/inactive) using a support vector machine and Codessa structural descriptors. We have now examined, using the same data set and with the same descriptors, the applicability and performance of five other machine learning approaches. These have been shown able to handle high dimensional data (with descriptors possibly irrelevant or redundant) and to cope with complex mechanisms, but without delivering explicit directly exploitable models. Splitting the data into five batches (training set 80%, test set 20%) and carrying out leave-one-out cross-validation, led to good results of comparable performance, consistent with our previous support vector classifier (SVC) results. Accuracy was greater than 0.80 for all approaches. A reduced set of 15 descriptors common to all the investigated approaches showed good predictive ability (confirmed using a three-layer perceptron) and gives some clues regarding a mechanistic interpretation.
authors.affiliation: a Itodys , Université Paris-Diderot , UMR 7086 , Paris , France.
authors.forename: J P
authors.initials: JP
authors.lastname: Doucet
authors.forename: A
authors.initials: A
authors.lastname: Doucet-Panaye
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D002265:Carboxylic Ester Hydrolases / Q000037:antagonists & inhibitors*; D003198:Computer Simulation; D004791:Enzyme Inhibitors / Q000737:chemistry / Q000145:classification / Q000494:pharmacology*; D006845:Hydrocarbons, Fluorinated / Q000737:chemistry / Q000145:classification / Q000494:pharmacology*; D009036:Moths / Q000187:drug effects / Q000201:enzymology; D013329:Structure-Activity Relationship
chemical_list: D004791:Enzyme Inhibitors; D006845:Hydrocarbons, Fluorinated; D002265:Carboxylic Ester Hydrolases; C038003:juvenile hormone esterase
keywords: SAR models; inhibitory activity; juvenile hormone esterase; machine learning methods; trifluoromethylketones