title: Relationship between molecular connectivity and carcinogenic activity: a confirmation with a new software program based on graph theory.
abstract: For a database of 826 chemicals tested for carcinogenicity, we fragmented the structural formula of the chemicals into all possible contiguous-atom fragments with size between two and eight (nonhydrogen) atoms. The fragmentation was obtained using a new software program based on graph theory. We used 80% of the chemicals as a training set and 20% as a test set. The two sets were obtained by random sorting. From the training sets, an average (8 computer runs with independently sorted chemicals) of 315 different fragments were significantly (p < 0.125) associated with carcinogenicity or lack thereof. Even using this relatively low level of statistical significance, 23% of the molecules of the test sets lacked significant fragments. For 77% of the molecules of the test sets, we used the presence of significant fragments to predict carcinogenicity. The average level of accuracy of the predictions in the test sets was 67.5%. Chemicals containing only positive fragments were predicted with an accuracy of 78.7%. The level of accuracy was around 60% for chemicals characterized by contradictory fragments or only negative fragments. In a parallel manner, we performed eight paired runs in which carcinogenicity was attributed randomly to the molecules of the training sets. The fragments generated by these pseudo-training sets were devoid of any predictivity in the corresponding test sets. Using an independent software program, we confirmed (for the complex biological endpoint of carcinogenicity) the validity of a structure-activity relationship approach of the type proposed by Klopman and Rosenkranz with their CASE program.
authors.affiliation: Istituto Nazionale per la Ricerca sul Cancro, Genova, Italy.
authors.forename: D
authors.initials: D
authors.lastname: Malacarne
authors.forename: R
authors.initials: R
authors.lastname: Pesenti
authors.forename: M
authors.initials: M
authors.lastname: Paolucci
authors.forename: S
authors.initials: S
authors.lastname: Parodi
mesh_terms: D002273:Carcinogens / Q000737:chemistry / Q000633:toxicity*; D003196:Computer Graphics; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D012984:Software; D013329:Structure-Activity Relationship
chemical_list: D002273:Carcinogens