Abstract:
:Monte Carlo based method by using either SMILES based or combination of SMILES and Graph-based descriptors is an important strategy to build the QSAR/QSTR model for prediction of different biological endpoints. In this study, Monte Carlo based QSTR approach was applied to the dataset of 90 nitroaromatic compounds related to their in vivo toxicity, represented by 50% lethal dose concentration for rats (LD50). Both classification and regression-based QSTR models were developed to get an idea about different fingerprints for promoters and hinderers of nitroaromatics toxicity. The best classification model was obtained by using SMILES and graph-based (GAO) descriptor with 1ECK connectivity (sensitivity = 0.7143, specificity = 1.0000, accuracy = 0.8889, and MCC = 0.7774). The best regression model calculated by using SMILES and hydrogen-suppressed graph descriptors with 0ECk connectivity (R2 = 0.7386, Q2 = 0.6315, S = 0.467, and MAE = 0.340). Finally, a consensus QSTR model was generated to predict efficiently the toxicity of new compounds. The study highlighted that the comparative QSTR models by using the Monte Carlo method can also be generated and will be a useful tool for structural fingerprint analysis in case of nitroaromatics for preliminary evaluation of its toxicity to mammals.
journal_name
Toxicol Mech Methodsjournal_title
Toxicology mechanisms and methodsauthors
Mondal D,Ghosh K,Baidya ATK,Gantait AM,Gayen Sdoi
10.1080/15376516.2019.1709238subject
Has Abstractpub_date
2020-05-01 00:00:00pages
257-265issue
4eissn
1537-6516issn
1537-6524journal_volume
30pub_type
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