Literature DB >> 15669692

On the nature, evolution and future of quantitative structure-activity relationships (QSAR) in toxicology.

G D Veith1.   

Abstract

The quantitative structure-activity relationship (QSAR) science agenda is being determined by its skeptics. Toxic substances control legislation over the past 30 years was born of a culture that tests animals and interprets the results of those tests in attempts to protect public health. Even with the current awareness that there are many more chemicals to assess than resources and test data permit, those skeptical of QSAR are predominant in the regulatory setting. Bureaucracies founded on laboratory testing, whether a private or governmental agency, will only begrudgingly accept QSAR as a strategic tool for designing chemicals and managing chemical risks. Every major milestone in QSAR accomplishments has been met with stronger skepticism that QSAR cannot replace animal testing. The QSAR research community needs to embrace the arguments of the skeptics and design research to overcome the perceived inadequacies of current QSAR methods. This paper will discuss three common errors in QSAR research, which, if corrected, will place in silico methods fully complementary to the strategic use of in vitro and in vivo methods. QSAR is based on well-defined endpoints of intrinsic chemical activities and molecular descriptors, which can be mechanistically interpreted. Chemicals in a QSAR training set ought to have a common mechanism of interaction so that the context of structural requirements defining the domain can be articulated and tested. Finally, the estimation of complex endpoints ought to be controlled by a QSAR-based expert system if the estimation of missing values or hazard screening in heterogeneous inventories is to avoid fueling the skepticism of QSAR.

Mesh:

Year:  2004        PMID: 15669692     DOI: 10.1080/10629360412331297380

Source DB:  PubMed          Journal:  SAR QSAR Environ Res        ISSN: 1026-776X            Impact factor:   3.000


  3 in total

1.  Utilizing high throughput screening data for predictive toxicology models: protocols and application to MLSCN assays.

Authors:  Rajarshi Guha; Stephan C Schürer
Journal:  J Comput Aided Mol Des       Date:  2008-02-19       Impact factor: 3.686

2.  Exploiting PubChem for Virtual Screening.

Authors:  Xiang-Qun Xie
Journal:  Expert Opin Drug Discov       Date:  2010-12       Impact factor: 6.098

3.  Mixed learning algorithms and features ensemble in hepatotoxicity prediction.

Authors:  Chin Yee Liew; Yen Ching Lim; Chun Wei Yap
Journal:  J Comput Aided Mol Des       Date:  2011-09-06       Impact factor: 3.686

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.