From QSAR to QSIIR: Searching for enhanced computational toxicology models

Hao Zhu

Research output: Chapter in Book/Report/Conference proceedingChapter

12 Scopus citations


Quantitative structure activity relationship (QSAR) is the most frequently used modeling approach to explore the dependency of biological, toxicological, or other types of activities/properties of chemicals on their molecular features. In the past two decades, QSAR modeling has been used extensively in drug discovery process. However, the predictive models resulted from QSAR studies have limited use for chemical risk assessment, especially for animal and human toxicity evaluations, due to the low predictivity of new compounds. To develop enhanced toxicity models with independently validated external prediction power, novel modeling protocols were pursued by computational toxicologists based on rapidly increasing toxicity testing data in recent years. This chapter reviews the recent effort in our laboratory to incorporate the biological testing results as descriptors in the toxicity modeling process. This effort extended the concept of QSAR to quantitative structure in vitro-in vivo relationship (QSIIR). The QSIIR study examples provided in this chapter indicate that the QSIIR models that based on the hybrid (biological and chemical) descriptors are indeed superior to the conventional QSAR models that only based on chemical descriptors for several animal toxicity endpoints. We believe that the applications introduced in this review will be of interest and value to researchers working in the field of computational drug discovery and environmental chemical risk assessment.

Original languageEnglish (US)
Title of host publicationComputational Toxicology
Subtitle of host publicationVolume II
Number of pages13
StatePublished - 2013

Publication series

NameMethods in Molecular Biology
ISSN (Print)1064-3745

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Genetics


  • Biological descriptors
  • Chemical descriptors
  • Compounds
  • Computational toxicology
  • HTS
  • Predictive model
  • QSAR


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