Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling.
The Carcinogenicity Reliability Database (CRDB) was constructed by collecting experimental carcinogenicity data on about 1,500 chemicals from six sources, including IARC, and NTP databases, and then by ranking their reliabilities into six unified categories. A wide variety of 911 organic chemicals were selected from the database for QSAR modeling, and 1,504 kinds of different molecular descriptors were calculated, based on their 3D molecular structures as modeled by the Dragon software. Positive (carcinogenic) and negative (non-carcinogenic) chemicals containing various substructures were counted using atom and functional group count descriptors, and the statistical significance of ratios of positives to negatives was tested for those substructures. Very few were judged to be strongly related to carcinogenicity, among substructures known to be responsible for carcinogens as revealed from biomedical studies. In order to develop QSAR models for the prediction of the carcinogenicities of a wide variety of chemicals with a satisfactory performance level, the relationship between the carcinogenicity data with improved reliability and a subset of significant descriptors selected from 1,504 Dragon descriptors was analyzed with a support vector machine (SVM) method: the classification function (SVC) for weighted data in LIBSVM program was used to classify chemicals into two carcinogenic categories (positive or negative), where weights were set depending on the reliabilities of the carcinogenicity data. The quality and stability of the models presented were tested by performing a dual cross-validation procedure. A single SVM model as the first step was developed for all the 911 chemicals using 250 selected descriptors, achieving an overall accuracy level, i.e., positive and negative correct estimate, of about 70%. In order to improve the accuracy of the final model, the 911 chemicals were classified into 20 mutually overlapping subgroups according to contained substructures, a specific SVM model was optimized for each subgroup, and the predicted carcinogenicities of the 911 chemicals were determined by the majorities of the outputs of the corresponding SVM models. The model developed on the basis of grouping of chemicals into 20 substructures predicts the carcinogenicities of a wide variety of chemicals with a satisfactory overall accuracy of approximately 80%.
Tanabe
Kazutoshi
K
Neuroscience Research Institute, National Institute of Advanced Industrial Science and Technology, Umezono 1-1-1, Tsukuba, 305-8568, Japan. k-tanabe@aist.go.jp
Lučić
Bono
B
Amić
Dragan
D
Kurita
Takio
T
Kaihara
Mikio
M
Onodera
Natsuo
N
Suzuki
Takahiro
T
D001185:Artificial Intelligence; D002273:Carcinogens / Q000737:chemistry* / Q000302:isolation & purification* / Q000494:pharmacology; D003198:Computer Simulation; D004526:Efficiency; D005544:Forecasting; D007287:Inorganic Chemicals / Q000737:chemistry / Q000302:isolation & purification* / Q000494:pharmacology; D015394:Molecular Structure; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results; D013647:Task Performance and Analysis; D054928:Validation Studies as Topic
D002273:Carcinogens; D007287:Inorganic Chemicals