toxicityprediction

This project is to predict toxicity of chemicals from EPA database by using different molecular description and machine learning approaches. (1):Computer-aided prediction of toxicity with substructure pattern and random forest download address: http://toxicityprediction.googlecode.com/files/fff.tif This part explores the toxicity information included in EPA datasets by using quantative strcuture-toxicity relationship (QSTR). The models mainly make use of substructure fingerprints and the state-of-the-art machine learning algorithm, called random forest, to obtain an in-depth insights into toxicity machinism. Toxicity of chemicals induced by different factors is an important consideration, especially during the drug research and development process. Thus there is urgent need to develop computationally effective models that can predict the toxicity or adverse effects of chemicals for a specific class of chemicals. In this study, ra