Rule extraction from a mutagenicity data set using adaptively grown phylogenetic-like trees.
A public bacterial mutagenicity database was classified into 2-D structural families using a set of specific algorithms and clustering techniques that find overlapping classes of compounds based upon chemical substructures. Structure-activity relationships were learned from the biological activity of the compounds within each class and used to identify rules that define substructures potentially responsible for mutagenic activity. In addition, this method of analysis was used to compare the pharmacologically relevant substructure of test compounds with their potential toxic substructures making this a potentially valuable in silico profiling tool for lead selection and optimization.
Bacha
Patricia A
PA
Bioreason, Inc, 150 Washington Ave, Suite 220, Santa Fe, New Mexico 87501, USA. bacha@bioreason.com
Gruver
Heather S
HS
Den Hartog
Bobi K
BK
Tamura
Susan Y
SY
Nutt
Ruth F
RF
D000465:Algorithms; D016208:Databases, Factual; D006801:Humans; D008956:Models, Chemical; D009152:Mutagenicity Tests / Q000706:statistics & numerical data*; D009153:Mutagens / Q000737:chemistry / Q000633:toxicity; D010802:Phylogeny; D013329:Structure-Activity Relationship
D009153:Mutagens