Sharma, Ashok K. published the artcileToxiM: a toxicity prediction tool for small molecules developed using machine learning and chemoinformatics approaches, Formula: C13H8Cl2O4S, the main research area is hepatotoxicity ToxiM machine learning chemoinformatics asbestos ethylene glycol; chemoinformatics; classification models; machine leaning; permeability; regression model; solubility; toxicity prediction.
The exptl. methods for the prediction of mol. toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of mol. toxicity along with the aqueous solubility and permeability of any mol./metabolite. Using a comprehensive and curated set of toxin mols. as a training set, the different chem. and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the mol. features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93%) and Matthews’s correlation coefficient (0.84). The performances of all the three models were comparable (Matthews’s correlation coefficient = 0.84-0.87) on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better (R2 = 0.84) than the multi-linear regression (MLR) and partial least square regression (PLSR) models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2) performed better (R2 = 0.68) in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity, solubility, and permeability of small mols.
Frontiers in Pharmacology published new progress about Asbestos Role: BSU (Biological Study, Unclassified), BIOL (Biological Study). 40180-04-9 belongs to class benzothiophene, name is 2-(2,3-Dichloro-4-(thiophene-2-carbonyl)phenoxy)acetic acid, and the molecular formula is C13H8Cl2O4S, Formula: C13H8Cl2O4S.
Referemce:
Benzothiophene – Wikipedia,
Benzothiophene | C8H6S – PubChem