ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches

Ashok K. Sharma, Gopal N. Srivastava, Ankita Roy, Vineet K. Sharma
2017 Frontiers in Pharmacology  
= 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 (R 2 = 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 (R 2 = 0.68) in
more » ... on 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 molecules.
doi:10.3389/fphar.2017.00880 pmid:29249969 pmcid:PMC5714866 fatcat:ziuwkjk3ebeita5atjivl5ugwq