Prediction of skin sensitization with a particle swarm optimized support vector machine.
Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT) and murine local lymph node assay (LLNA) are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structure-activity relationships (QSARs) are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers.
Yuan
Hua
H
Key Laboratory of Theoretical Chemistry and Molecular Simulation of Ministry of Education, Hunan University of Science and Technology, Xiangtan 411201, China.|Hunan Provincial University Key Laboratory of QSAR/QSPR, Xiangtan 411201, China.|School of Chemistry and Chemical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China.
Huang
Jianping
J
Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310027, China.
Cao
Chenzhong
C
Key Laboratory of Theoretical Chemistry and Molecular Simulation of Ministry of Education, Hunan University of Science and Technology, Xiangtan 411201, China.|Hunan Provincial University Key Laboratory of QSAR/QSPR, Xiangtan 411201, China.|School of Chemistry and Chemical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China.
D000485:Allergens / Q000737:chemistry* / Q000633:toxicity; D009783:Dermatitis, Occupational / Q000209:etiology; D006801:Humans; D021281:Quantitative Structure-Activity Relationship; D054852:Small Molecule Libraries / Q000737:chemistry* / Q000633:toxicity; D060388:Support Vector Machine
D000485:Allergens; D054852:Small Molecule Libraries