Random forest chemical function predictions for Tox21 chemicals in personal care products uses and "other" uses. This dataset is associated with the following publication: Isaacs , K., M. Goldsmith, P. Egeghy , K. Phillips, R. Brooks, T. Hong, and J. Wambaugh. Characterization and prediction of chemical functions and weight fractions in consumer products. Toxicology Reports. Elsevier B.V., Amsterdam, NETHERLANDS, 3: 723-732, (2016).

Merged product weight fraction and chemical function data. This dataset is associated with the following publication: Isaacs , K., M. Goldsmith, P. Egeghy , K. Phillips, R. Brooks, T. Hong, and J. Wambaugh. Characterization and prediction of chemical functions and weight fractions in consumer products. Toxicology Reports. Elsevier B.V., Amsterdam, NETHERLANDS, 3: 723-732, (2016).

Merged product weight fraction and chemical function data. This dataset is associated with the following publication: Isaacs , K., M. Goldsmith, P. Egeghy , K. Phillips, R. Brooks, T. Hong, and J. Wambaugh. Characterization and prediction of chemical functions and weight fractions in consumer products. Toxicology Reports. Elsevier B.V., Amsterdam, NETHERLANDS, 3: 723-732, (2016).

Chemicals and harmonized functions - dataset of chemicals mapped to a harmonized chemical function category. This dataset is associated with the following publication: Isaacs , K., M. Goldsmith, P. Egeghy , K. Phillips, R. Brooks, T. Hong, and J. Wambaugh. Characterization and prediction of chemical functions and weight fractions in consumer products. Toxicology Reports. Elsevier B.V., Amsterdam, NETHERLANDS, 3: 723-732, (2016).

An elicitation with 32 experts informed relative prioritization of risks from chemical properties and human use factors for consumer product-related chemicals. Three different versions of the model were evaluated using distinct weight profiles. This dataset is associated with the following publication: Wood, M., K. Plourde, S. Larkin, P. Egeghy, A. Williams, V. Zemba, I. Linkov, and D. Vallero. Advances on a Decision Analytic Approach to Exposure‐Based Chemical Prioritization. RISK ANALYSIS. Blackwell Publishing, Malden, MA, USA, 40(1): 83-96, (2020).

Data and model predictions supporting the manuscript: Isaacs K.K., Phillips K.A., Biryol D., Dionisio K.L., and Price P. Consumer product chemical weight fractions from ingredient lists. Journal of Exposure Science and Environmental Epidemiology (in press as of 8/2017). This dataset is associated with the following publication: Isaacs, K., K. Phillips, D. Biryol, K. Dionisio, and P. Price. Consumer product chemical weight fractions from ingredient lists. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 28: 216-222, (2018).

QSAR Model Reporting Formats. Examples of R code: feature selection and regression analysis. Figure S1: Data distribution of logBCF, BP, MP and logVP. Figures S2–S5: Relationship between model complexity and prediction errors as well as the plots of estimated values versus experimental data for logBCF, BP, MP, and logVP, respectively. Figure S6: Plots of leverage versus standardized residuals for logBCF, BP, MP, and logVP models. Table S1: Chemical product classes for training and test sets. Tables S2–S5: Regression statistics for logBCF, BP, MP, and logVP, respectively. Table S6: Applicability domains for logBCF, BP, MP, and logVP. Tables S7–S12: Chemicals with large prediction residuals for the six properties (PDF) Chemical names, CAS registry number and SMILES as well as experimentally measured and estimated property values of the training and test sets (XLSX). This dataset is associated with the following publication: Zang, Q., K. Mansouri, A. Williams, R. Judson, D. Allen, W.M. Casey, and N.C. Kleinstreuer. (Journal of Chemical Information and Modeling) In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning. Journal of Chemical Information and Modeling. American Chemical Society, Washington, DC, USA, 57(1): 36-49, (2017).

The Chemical and Product Categories database (CPCat) catalogs the use of over 40,000 chemicals and their presence in different consumer products. The chemical use information is compiled from multiple sources while product information is gathered from publicly available Material Safety Data Sheets (MSDS). EPA researchers are evaluating the possibility of expanding the database with additional product and use information.

The dataset and experimental and predicted amenability calls are provided in the supplemental file “Supplemental_ToxCast_PhaseII.xlsx”. PaDEL descriptors were generated for each candidate and amenability predictions were calculated using both ESI+ and ESI- downsampled models. The resulting dataset is available in the supplemental file “Supplemental_Application.xlsx”. It should be noted that the dataset used in this demonstration is biased toward environmentally relevant chemicals, many of which appear in a large number of chemical lists on the Dashboard (see the DATA_SOURCES column in “Supplemental_Application.xlsx” for both ESI+ and ESI-). Training and test datasets were constructed using the PaDEL descriptors and the ESI+ and ESI- endpoint values discussed previously. These training and test sets are provided in the supplemental file “Supplemental_train_test.xlsx”. A list of descriptors is provided in the supplemental file “Supplemental_Descriptors.xlsx”. A similar plot (Figure S1) of variable importance for the ESI+ upsampled model, and a similar plot (Figure S2) of variable importance for the ESI- upsampled model can be found in “Supplemental_Figures.docx”. This dataset is associated with the following publication: Lowe, C., K. Isaacs, A. McEachran, C. Grulke, J. Sobus, E. Ulrich, A. Richard, A. Chao, J. Wambaugh, and A. Williams. Predicting compound amenability with liquid chromatography-mass spectrometry to improve non-targeted analysis. Analytical and Bioanalytical Chemistry. Springer, New York, NY, USA, 413(30): 7495-7508, (2021).

The dataset and experimental and predicted amenability calls are provided in the supplemental file “Supplemental_ToxCast_PhaseII.xlsx”. PaDEL descriptors were generated for each candidate and amenability predictions were calculated using both ESI+ and ESI- downsampled models. The resulting dataset is available in the supplemental file “Supplemental_Application.xlsx”. It should be noted that the dataset used in this demonstration is biased toward environmentally relevant chemicals, many of which appear in a large number of chemical lists on the Dashboard (see the DATA_SOURCES column in “Supplemental_Application.xlsx” for both ESI+ and ESI-). Training and test datasets were constructed using the PaDEL descriptors and the ESI+ and ESI- endpoint values discussed previously. These training and test sets are provided in the supplemental file “Supplemental_train_test.xlsx”. A list of descriptors is provided in the supplemental file “Supplemental_Descriptors.xlsx”. A similar plot (Figure S1) of variable importance for the ESI+ upsampled model, and a similar plot (Figure S2) of variable importance for the ESI- upsampled model can be found in “Supplemental_Figures.docx”. This dataset is associated with the following publication: Lowe, C., K. Isaacs, A. McEachran, C. Grulke, J. Sobus, E. Ulrich, A. Richard, A. Chao, J. Wambaugh, and A. Williams. Predicting compound amenability with liquid chromatography-mass spectrometry to improve non-targeted analysis. Analytical and Bioanalytical Chemistry. Springer, New York, NY, USA, 413(30): 7495-7508, (2021).