Quantitative Regression Models for the Prediction of Chemical Properties by an Efficient Workflow
Rapid safety assessment is more and more needed for the increasing chemicals both in chemical industries and regulators around the world.The traditional experimental methods couldn”t meet the current demand any more.With the development of the information technology and the growth of experimental data,in silico modeling has become a practical and rapid alternative for the assessment of chemical properties,especially for the toxicity prediction of organic chemicals.In this study,a quantitative regression workflow was built by KNIME to predict chemical properties.With this regression workflow,quanti-tative values of chemical properties can be obtained,which is different from the binary-classification model or multi-classification models that can only give qualitative results.To illustrate the usage of the workflow,two predictive models were constructed based on datasets of Tetrahymena pyriformis toxicity and Aqueous solubility.The qcv2 and qtest2 of 5-fold cross validation and external validation for both types of models were greater than 0.7,which implies that our models are robust and reliable,and the workflow is very convenient and efficient in prediction of various chem-ical properties.
Quantitative regression models KNIME workflow Toxicity prediction Aqueous solubility
Yongmin Yin Congying Xu Shikai Gu Weihua Li Guixia Liu Yun Tang
Shanghai Key Laboratory of New Drug Design,School of Pharmacy,East China University of Science and Technology Shanghai 200237,RR,China
国内会议
大连
英文
715-724
2016-09-24（万方平台首次上网日期，不代表论文的发表时间）