Linear Regression, Model Averaging, and Bayesian Techniques for Predicting Chemical Activities from Structure

Supplemental Files
Date
2016-01-01
Authors
Niemi, Jarad
Niemi, Gerald
Niemi, Jarad
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Statistics
Organizational Unit
Journal Issue
Series
Department
Statistics
Abstract

A primary goal of quantitative structure-activity relationships (QSARs) and quantitative structure-property relationships (QSPRs) is to predict chemical activities from chemical structure. Chemical structure can be quantified in many ways resulting in hundreds, if not thousands, of measurements for every chemical. Chemical activities measures how the chemical interacts with other chemicals, e.g. toxicity, biodegradability, boiling point, and vapor pressure. Typically there are more chemical structure measurements than chemicals being measured, the so-called large-p, small-n problem. Here we review some of the statistical procedures that have been commonly used to explore these problems in the past and provide several examples of their use. Finally, we peek into the future to discuss two areas that we believe will see dramatically increased attention in the near future: model averaging and Bayesian techniques.

Comments

This is a manuscript of a chapter from Advances in Mathematical Chemistry and Applications 2 (2016): 125, doi:10.2174/9781681080529115020010. Posted with permission.

Description
Keywords
Citation
DOI
Collections