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QSAR Classification of ToxCast and Tox21 Chemicals on the Basis of Estrogen Receptor Assays (FutureToxII)
Zang, D., D. Rotroff, AND R. Judson. QSAR Classification of ToxCast and Tox21 Chemicals on the Basis of Estrogen Receptor Assays (FutureToxII). Presented at FutureTox II, Chapel Hill, NC, January 16 - 17, 2014. https://doi.org/10.23645/epacomptox.5197144
The present work uses this large in vitro data set to develop in silico QSAR models using machine learning (ML) methods and a novel approach to manage the imbalanced data sets seen in all targets we have tested.
The ToxCast and Tox21 programs have tested ~8,200 chemicals in a broad screening panel of in vitro high-throughput screening (HTS) assays for estrogen receptor (ER) agonist and antagonist activity. The present work uses this large in vitro data set to develop in silico QSAR models using machine learning (ML) methods and a novel approach to manage the imbalanced data sets seen in all targets we have tested. Training compounds from the ToxCast project were classified as active or inactive based on a composite ER Interaction Score derived from a collection of 13 ER in vitro assays. A total of 1,537 chemicals from ToxCast were used to derive and optimize the binary classification models while 5,073 additional chemicals from the Tox21 project were used to externally validate the model performance. QSAR classification models were built to relate the molecular structures of chemicals to their ER activities using linear discriminant analysis (LDA), classification and regression trees (CART), and support vector machines (SVM) with 51 molecular descriptors from QikProp and 4328 structural fingerprints as explanatory variables. A random forest (RF) feature selection method was used to extract the structural features most relevant to ER activity. The performance was evaluated using various metrics, including overall accuracy, sensitivity, specificity, G-mean, as well as area under the receiver operating characteristic (ROC) curve (AUC). The best model was obtained using SVM in combination with a set of descriptors identified from a large set via the RF algorithm, which recognized the active and inactive compounds at the accuracies of 76.1% and 82.8% with a total accuracy of 81.6% on the internal test set and 70.8% on the external test set. These results demonstrate that a combination of high-quality experimental data and ML methods can lead to robust models that achieve excellent predictive accuracy, which are potentially useful for facilitating the virtual screening of chemicals for environmental risk assessment. This abstract does not necessarily reflect U.S. EPA policy.
POSTER FOR FUTURETOX - DANZANG.PDF (PDF,NA pp, 343.882 KB, about PDF)
ZANG-ABSTRACT.PDF (PDF,NA pp, 102.704 KB, about PDF)
Record Details:Record Type: DOCUMENT (PRESENTATION/POSTER)
Organization:U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
NATIONAL CENTER FOR COMPUTATIONAL TOXICOLOGY