title: QSAR classification of metabolic activation of chemicals into covalently reactive species.
abstract: Metabolic activation of chemicals into covalently reactive species might lead to toxicological consequences such as tissue necrosis, carcinogenicity, teratogenicity, or immune-mediated toxicities. Early prediction of this undesirable outcome can help in selecting candidates with increased chance of success, thus, reducing attrition at all stages of drug development. The ensemble modelling of mixed features was used for the development of a model to classify the metabolic activation of chemicals into covalently reactive species. The effects of the quality of base classifiers and performance measure for sorting were examined. An ensemble model of 13 naive Bayes classifiers was built from a diverse set of 1,479 compounds. The ensemble model was validated internally with five-fold cross validation and it has achieved sensitivity of 67.4% and specificity of 93.4% when tested on the training set. The final ensemble model was made available for public use.
authors.affiliation: Department of Pharmacy, National University of Singapore, Singapore. liewchinyee@gmail.com
authors.forename: Chin Yee
authors.initials: CY
authors.lastname: Liew
authors.forename: Chuen
authors.initials: C
authors.lastname: Pan
authors.forename: Andre
authors.initials: A
authors.lastname: Tan
authors.forename: Ke Xin Magneline
authors.initials: KX
authors.lastname: Ang
authors.forename: Chun Wei
authors.initials: CW
authors.lastname: Yap
mesh_terms: D001711:Biotransformation; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000737:chemistry / Q000378:metabolism*; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results
chemical_list: D004364:Pharmaceutical Preparations