Use of a composite polyfunctional model electrophile as a probe to analyze the performance of an artificial intelligence structure-activity method.
The CASE structure-activity relational method was applied to the model polyfunctional electrophile proposed by Ashby and associates. The predicted activities from data bases of 'structural alerts', mutagenicity in Salmonella and rodent carcinogenicity were compared. It was thus found that the predictive efficacy of CASE was increased when it employed a combination of human and artificial intelligence, as exemplified by the CASE analysis of 'structural alerts.
Rosenkranz
H S
HS
Department of Environmental Health Sciences, Case Western Reserve University, Cleveland, OH 44106.
Klopman
G
G
D000818:Animals; D001185:Artificial Intelligence; D015197:Carcinogenicity Tests; D002273:Carcinogens; D008958:Models, Molecular; D009152:Mutagenicity Tests; D009153:Mutagens / Q000633:toxicity*; D012377:Rodentia; D012475:Salmonella / Q000235:genetics; D013329:Structure-Activity Relationship
D002273:Carcinogens; D009153:Mutagens