title: In silico prediction of the mutagenicity of nitroaromatic compounds using a novel two-QSAR approach.
abstract: Certain drugs are nitroaromatic compounds, which are potentially toxic. As such, it is of practical importance to assess and predict their mutagenic potency in the process of drug discovery. A classical quantitative structure-activity relationship (QSAR) model was developed using the linear partial least square (PLS) scheme to understand the underline mutagenic mechanism and a non-classical QSAR model was derived using the machine learning-based hierarchical support vector regression (HSVR) to predict the mutagenicity of nitroaromatic compounds based on a series of mutagenicity data (TA98-S9). It was observed that HSVR performed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical validations. A mock test designated to mimic real challenges also confirmed the better performance of HSVR. Furthermore, HSVR exhibited superiority in predictivity, generalization capabilities, consistent performance, and robustness when compared with various published predictive models. PLS, conversely, revealed some mechanistically interpretable relationships between descriptors and mutagenicity. Thus, this two-QSAR approach using the predictive HSVR and interpretable PLS models in a synergistic fashion can be adopted to facilitate drug discovery and development by designing safer drug candidates with nitroaromatic moiety.
authors.affiliation: Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 97401, Taiwan.
authors.forename: Yi-Lung
authors.initials: YL
authors.lastname: Ding
authors.affiliation: Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 97401, Taiwan.
authors.forename: You-Chen
authors.initials: YC
authors.lastname: Lyu
authors.affiliation: Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 97401, Taiwan; Department of Life Science and Institute of Biotechnology, National Dong Hwa University, Shoufeng, Hualien 97401, Taiwan. Electronic address: leong@mail.ndhu.edu.tw.
authors.forename: Max K
authors.initials: MK
authors.lastname: Leong
mesh_terms: D003198:Computer Simulation; D055808:Drug Discovery; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D009152:Mutagenicity Tests; D009153:Mutagens / Q000737:chemistry* / Q000633:toxicity*; D009574:Nitro Compounds / Q000737:chemistry* / Q000633:toxicity*; D021281:Quantitative Structure-Activity Relationship; D012486:Salmonella typhimurium / Q000187:drug effects / Q000235:genetics; D060388:Support Vector Machine
chemical_list: D009153:Mutagens; D009574:Nitro Compounds
keywords: 2-QSAR; Hierarchical support vector regression; Mutagenicity; Nitroaromatic compounds; Partial least square