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

Jarad B. Niemi, Gerald J. Niemi

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish (US)
Title of host publicationAdvances in Mathematical Chemistry and Applications
PublisherElsevier Inc.
Pages125-147
Number of pages23
Volume2
ISBN (Electronic)9781681080529
ISBN (Print)9781681080536
DOIs
StatePublished - 2015

Bibliographical note

Publisher Copyright:
© 2015 Bentham Science Publishers Ltd Published by Elsevier Inc. All rights reserved.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • AIC
  • BIC
  • Bayesian analysis
  • Cross-validation
  • Elastic net
  • K-means clustering
  • LASSO
  • Model averaging
  • Model selection
  • Modeling
  • Partial least squares
  • Prediction
  • Principal component analysis
  • Principal component regression
  • Regression
  • Ridge regression

Fingerprint

Dive into the research topics of 'Linear Regression, Model Averaging, and Bayesian Techniques for Predicting Chemical Activities from Structure'. Together they form a unique fingerprint.

Cite this