EPA Science Inventory

Domain-Specific QSAR Models for Identifying Potential Estrogenic Activity of Phenols (FutureTox III)

Citation:

Mansouri, K., R. Politi, S. Farag, E. Muratov, A. Tropsha, R. Judson, Q. Zang, D. Allen, W. Casey, AND N. Kleinstreuer. Domain-Specific QSAR Models for Identifying Potential Estrogenic Activity of Phenols (FutureTox III). Presented at FutureTox III, Research Triangle Park, NC, November 19 - 20, 2015. https://doi.org/10.23645/epacomptox.5189338

Description:

Computational tools can be used for efficient evaluation of untested chemicals for their ability to disrupt the endocrine system. We have employed previously developed global QSAR models that were trained and validated on the ToxCast/Tox21 ER assay data for virtual screening of a large library of ~30,000 industrial chemicals. Compounds predicted to have estrogen receptor (ER) binding and agonist activity included a high proportion of phenolic compounds, consistent with prior knowledge on the influence of this structural moiety on chemical interaction with the ER. However, global models did not accurately predict specific activity and relative potency of various phenols. Thus, we constructed local QSAR models focused on this chemical category only in the ToxCast/Tox21 data. Models developed with random forest and partial least squares discriminant analysis methods were trained and tested on the data from both ToxCast/Tox21 assays and well-curated literature sources. Local models consistently yielded higher balanced accuracies, sensitivity, and specificity than global models as evaluated on the external test sets. Our results suggest that these models can be used as reliable support tools for evaluating the endocrine disrupting potential of environmental phenolic chemicals. This work does not reflect EPA policy. This project was funded in whole or in part with Federal funds from the NIEHS, NIH under Contract No. HHSN273201500010C.

Purpose/Objective:

Poster presentation at the FutureTox III meeting. We constructed local QSAR models focused on this chemical category only in the ToxCast/Tox21 data. Models developed with random forest and partial least squares discriminant analysis methods were trained and tested on the data from both ToxCast/Tox21 assays and well-curated literature sources

URLs/Downloads:

https://doi.org/10.23645/epacomptox.5189338   Exit

Record Details:

Record Type: DOCUMENT (PRESENTATION/POSTER)
Product Published Date: 11/19/2015
Record Last Revised: 01/11/2018
OMB Category: Other
Record ID: 324259

Organization:

U.S. ENVIRONMENTAL PROTECTION AGENCY

OFFICE OF RESEARCH AND DEVELOPMENT

NATIONAL CENTER FOR COMPUTATIONAL TOXICOLOGY