ToxiPred: A server for prediction of aqueous toxicity of small chemical molecules in T. pyriformis
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Identification of non-toxic drug design is a major challenge in the field of drug design, most of the drug failure due to toxicity being found in late development or even in clinical trials. Thus the use of predictive toxicology is called for. Keeping this problem in view, several QSAR methods have been employed previously but we have started this study with latest dataset and apply different machine learning classifiers including non-linear method implemented in WEKA and linear method (Multiple linear regressions (MLR)) using R-package.
Toxipred is a server where user can submit chemical molecules in the commonly used format (mol/SMILE/sdf) and after descriptors calculation our server would predict the pIGC50 value of the molecule. We hope that present model will aid in the area of drug designing.