Quantitative Regression Models for the Prediction of Chemical Properties by an Efficient Workflow. Issue 10 (14th July 2015)

• Record Type: Journal Article
• Authors: Yin, Yongmin; Xu, Congying; Gu, Shikai; Li, Weihua; Liu, Guixia; Tang, Yun
• Is Part Of: Molecular informatics. Volume 34:Issue 10(2015:Oct.)
• Page Start: 679
• Page End: 688
• Publication Date: 2015-07-14
• Subjects: Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.201400119
• Languages: English
• ISSNs: 1868-1743

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <p>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, quantitative 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 <italic>Tetrahymena pyriformis</italic> toxicity and Aqueous solubility. The <italic>q</italic>_cv² and <italic>q</italic>_test² 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 chemical properties.</p> </abstract>