Neural Network Prediction of Carcinogenicity of Diverse Organic Compounds

Kazutoshi TANABEa*, Norihito OHMORIa, Shuichiro ONOa, Takahiro SUZUKIb, Takatoshi MATSUMOTOc, Umpei NAGASHIMAd and Hiroyuki UESAKAe

aDepartment of Management Information Science, Chiba Institute of Technology
Tsudanuma 2-17-1, Narashino, Chiba 275-0016, Japan
bFaculty of Economics, Toyo University
Hakusan 5-28-20, Bunkyo-ku, Tokyo 112-8606, Japan
cInstitute of Multidisciplinary Research for Advanced Materials, Tohoku University
Katahira 2-1-1, Aoba, Sendai, Miyagi 980-8577, Japan
dGrid Research Center, National Institute of Advanced Industrial Science and Technology
Umezono 1-1-1, Tsukuba, Ibaraki 305-8568, Japan
eDepartment of Regional Science, Toyama University of International Studies
Higashikuromaki 65-1, Ohyama, Kamishinkawa, Toyama 930-1292, Japan

(Received: August 27, 2004; Accepted for publication: June 20, 2005; Published on Web: August 31, 2005)

A three-layered neural network model to predict the hazards of a variety of compounds based on a quantitative structure-activity relationship was developed. The inputs were 10 principal components from 37 kinds of molecular descriptors calculated with MO programs. For the output the data used in the Predictive Toxicology Challenge (PTC) 2000-2001 contest were employed, containing 454 compounds with the carcinogenic activity of male rats. The total database of 454 compounds was split into training (144 compounds), validation (143) and test (167) sets. To solve the problems such as over-training, over-fitting and local minimum in training the neural network with the error-back-propagation algorithm, various conditions of the network such as the training cycles and neuron numbers of the intermediate layer were optimized. The optimum model showed a correct classification rate close to 74 %, higher than any of the PTC contestants.

Keywords: Quantitative structure-activity relationship, Neural network, Carcinogenicity prediction, Principal component analysis, Over-training

Abstract in Japanese

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