Predictive carcinogenicity: a model for aromatic compounds, with nitrogen-containing substituents, based on molecular descriptors using an artificial neural network.
A back-propagation neural network to predict the carcinogenicity of aromatic nitrogen compounds was developed. The inputs were molecular descriptors of different types: electrostatic, topological, quantum-chemical, physicochemical, etc. For the output the index TD50 as introduced by Gold and colleagues was used, giving a continuous numerical parameter expressing carcinogenicity. From the tens of descriptors calculated, principal component analysis enabled us to restrict the number of parameters to be used for the artificial neural network (ANN). We used 104 molecules for the study. An Rcv2 = 0.69 was obtained. After removal of 12 outliers, a new ANN gave an Rcv2 of 0.82.
Gini
G
G
Dipartimento di Elettronica e Informazione, Politecnico di Milano, Italy.
Lorenzini
M
M
Benfenati
E
E
Grasso
P
P
Bruschi
M
M
D015197:Carcinogenicity Tests; D002273:Carcinogens / Q000737:chemistry / Q000633:toxicity*; D008956:Models, Chemical; D016571:Neural Networks, Computer; D009584:Nitrogen / Q000737:chemistry; D013329:Structure-Activity Relationship
D002273:Carcinogens; D009584:Nitrogen