title: Carcinogenicity prediction of noncongeneric chemicals by augmented top priority fragment classification.
abstract: Carcinogenicity prediction is an important process that can be performed to cut down experimental costs and save animal lives. The current reliability of the results is however disputed. Here, a blind exercise in carcinogenicity category assessment is performed using augmented top priority fragment classification. The procedure analyses the applicability domain of the dataset, allocates in clusters the compounds using a leading molecular fragment, and a similarity measure. The exercise is applied to three compound datasets derived from the Lois Gold Carcinogenic Database. The results, showing good agreement with experimental data, are compared with published ones. A final discussion on our viewpoint on the possibilities that the carcinogenicity modelling of chemical compounds offers is presented.
authors.affiliation: Department of Chemistry, Materials, and Chemical Engineering "Giulio Natta", Via Mancinelli 7, I-20131 Milano, Italy. Electronic address: mose1.casalegno@polimi.it.
authors.forename: Mosè
authors.initials: M
authors.lastname: Casalegno
authors.affiliation: Dipartimento di Chimica, Università degli Studi di Milano, via Golgi 19, I-20133 Milano, Italy. Electronic address: guido.sello@unimi.it.
authors.forename: Guido
authors.initials: G
authors.lastname: Sello
mesh_terms: D015197:Carcinogenicity Tests; D002273:Carcinogens / Q000633:toxicity*; D013329:Structure-Activity Relationship
chemical_list: D002273:Carcinogens
keywords: Carcinogen classes; Carcinogenicity prediction; Functional groups; Molecular fragments; Structural alerts; Structure–activity relationships