Structure-activity studies of barbiturates using pattern recognition techniques.
The relationship between molecular structure and duration of depressant effect for barbiturates was investigated. A data set of 160 5,5'-disubstituted barbiturates with various acyclic substituents was coded using 47 numerical descriptors including fragments, substructures, environmental descriptors, and molecular connectivity indexes. All descriptors were derived directly from the connection tables of the barbiturates. Using an interactive error-correction feedback algorithm, linear discriminant functions were developed that could dichotomize the data set with respect to several thresholds separating longer from shorter acting compounds. Feature selection was used to focus on the relatively few structural descriptors sufficient to support linear separability. For three specific thresholds, nine, 11, and nine descriptors were sufficient. The importance of these descriptors and the utility of the technique are discussed. Predictive abilities of approximately 94% were obtained for known barbiturates of the same general molecular types.
D000818:Animals; D001463:Barbiturates / Q000494:pharmacology*; D055598:Chemical Phenomena; D002627:Chemistry, Physical; D003864:Depression, Chemical; D051379:Mice; D008954:Models, Biological; D011817:Rabbits; D051381:Rats; D013329:Structure-Activity Relationship
D001463:Barbiturates