The dataset has all of the information used to create and evaluate 3 independent QSAR models for the fraction of a chemical unbound by plasma protein (Fub) for environmentally relevant chemicals. In vitro plasma protein values for 1245 pharmaceuticals and 406 ToxCast chemicals were collected from the literature (Obach 2008, Zhu 2013, Wetmore 2012, Wetmore 2015). The 21 descriptors calculated by MOE that were used in the models are included, as is an acid/base/neutral/zwitterions classification based on ionization percentages calculated in ADMET Predictor. Finally, the dataset includes the in silico Fub predictions for each chemical from the constructed k-nearest neighbor, support vector machine, and random forest QSAR models, as well as a consensus (average) prediction. This dataset is associated with the following publication: Ingle, B., R. Tornero-Velez, J. Nichols, and B. Veber. Informing the Human Plasma Protein Binding of Environmental Chemicals by Machine Learning in the Pharmaceutical Space: Applicability Domain and Limits of Predictability. Journal of Chemical Information and Modeling. American Chemical Society, Washington, DC, USA, 56(11): 2243-2252, (2016).

The MS Excel file (Dawson et al S2 Supporting information.xlsx) contains multiple sheets containing the training sets, test sets, and predictions for intrinsic metabolic clearance (Clint), fraction unbound in plasma (fup), and bioactivity-exposure ratios (BER), for ToxCast and pharmaceutical-like chemicals. The Word file (Dawson et al S1 Supporting Information.docx) provides additional supporting information on assembly of the training and test sets for Clint, fup, and BER. The data dictionary describes the terms used in the supporting information, S1 and S2. This dataset is associated with the following publication: Dawson, D., B. Ingle, K. Phillips, J. Nichols, J. Wambaugh, and R. Tornero-Velez. Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 55(9): 6505-6517, (2021).

The MS Excel file (Dawson et al S2 Supporting information.xlsx) contains multiple sheets containing the training sets, test sets, and predictions for intrinsic metabolic clearance (Clint), fraction unbound in plasma (fup), and bioactivity-exposure ratios (BER), for ToxCast and pharmaceutical-like chemicals. The Word file (Dawson et al S1 Supporting Information.docx) provides additional supporting information on assembly of the training and test sets for Clint, fup, and BER. The data dictionary describes the terms used in the supporting information, S1 and S2. This dataset is associated with the following publication: Dawson, D., B. Ingle, K. Phillips, J. Nichols, J. Wambaugh, and R. Tornero-Velez. Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 55(9): 6505-6517, (2021).

Additional details used in the methods are found in the MS Word file “S1_Dawson et al._Supporting_Information.docx”. The MS Excel file “S2_Dawson et al. Supporting Information.xlsx” contains datasets and graphical results. The Excel file sheets are as follows: S2.1 illustrates Clint hepatic flow calculations, S2.2 - 5 include training and test data sets; S2.6-7 include figures illustrating Clint model selection criteria and assemblages of model descriptors; S2.8 includes confusion matrices for evaluation Clint model, S2.9-10 include figures illustrating fup model selection criteria and assemblages of model descriptors (with ranges); S2.11 includes tables of model assessments of the Clint test set, S2.12 includes information relevant to BER calculations for the ToxCast test set, S2.13 includes information relevant to BER calculations for Tox21 chemicals, and S2.14 provides information on different transformations for fup. This dataset is associated with the following publication: Dawson, D., B. Ingle, K. Phillips, J. Nichols, J. Wambaugh, and R. Tornero-Velez. Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 55(9): 6505, (6517).

Additional details used in the methods are found in the MS Word file “S1_Dawson et al._Supporting_Information.docx”. The MS Excel file “S2_Dawson et al. Supporting Information.xlsx” contains datasets and graphical results. The Excel file sheets are as follows: S2.1 illustrates Clint hepatic flow calculations, S2.2 - 5 include training and test data sets; S2.6-7 include figures illustrating Clint model selection criteria and assemblages of model descriptors; S2.8 includes confusion matrices for evaluation Clint model, S2.9-10 include figures illustrating fup model selection criteria and assemblages of model descriptors (with ranges); S2.11 includes tables of model assessments of the Clint test set, S2.12 includes information relevant to BER calculations for the ToxCast test set, S2.13 includes information relevant to BER calculations for Tox21 chemicals, and S2.14 provides information on different transformations for fup. This dataset is associated with the following publication: Dawson, D., B. Ingle, K. Phillips, J. Nichols, J. Wambaugh, and R. Tornero-Velez. Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 55(9): 6505, (6517).

Observed fup data obtained from the literature; SMILES structures obtained from Pubchem SDF files. This dataset is associated with the following publication: Yun, Y.E., R. Tornero-Velez, T. Purucker, D. Chang, and A.N. Edginton. Evaluation of Quantitative Structure Property Relationship algorithms for predicting plasma protein binding in humans. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 17: 100142, (2021).

Observed fup data obtained from the literature; SMILES structures obtained from Pubchem SDF files. This dataset is associated with the following publication: Yun, Y.E., R. Tornero-Velez, T. Purucker, D. Chang, and A.N. Edginton. Evaluation of Quantitative Structure Property Relationship algorithms for predicting plasma protein binding in humans. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 17: 100142, (2021).

In this study, a major effort was undertaken to compile a large genotoxicity dataset (54,805 records for 9299 substances) from several public sources (e.g., TOXNET, COSMOS, eChemPortal). The names and outcomes of the different assays were harmonized, and assays were annotated by type: gene mutation in Salmonella bacteria (Ames assay) and chromosome mutation (clastogenicity) in vitro or in vivo (chromosome aberration, micronucleus, and mouse lymphoma Tk+/- assays). This dataset was then evaluated to assess genotoxic potential using a categorization scheme, whereby a substance was considered genotoxic if it was positive in at least one Ames or clastogen study. The categorization dataset comprised 8442 chemicals, of which 2728 chemicals were genotoxic, 5585 were not and 129 were inconclusive. QSAR models (TEST and VEGA) and the OECD Toolbox structural alerts/profilers (e.g., OASIS DNA alerts for Ames and chromosomal aberrations) were used to make in silico predictions of genotoxicity potential. The performance of the individual QSAR tools and structural alerts resulted in balanced accuracies of 57-73%. A Naïve Bayes consensus model was developed using combinations of QSAR models and structural alert predictions. The ‘best’ consensus model selected had a balanced accuracy of 81.2%, a sensitivity of 87.24% and a specificity of 75.20%. This in silico scheme offers promise as a first step in ranking thousands of substances as part of a prioritization approach for genotoxicity. This dataset is associated with the following publication: Pradeep, P., R. Judson, D. DeMarini, N. Keshava, T. Martin, J. Dean, C. Gibbons, A. Simha, S. Warren, M. Gwinn, and G. Patlewicz. An Evaluation of Existing QSAR Models and Structural Alerts and Development of New Ensemble Models for Genotoxicity Using a Newly Compiled Experimental Dataset. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 18: 100167, (2021).

In this study, a major effort was undertaken to compile a large genotoxicity dataset (54,805 records for 9299 substances) from several public sources (e.g., TOXNET, COSMOS, eChemPortal). The names and outcomes of the different assays were harmonized, and assays were annotated by type: gene mutation in Salmonella bacteria (Ames assay) and chromosome mutation (clastogenicity) in vitro or in vivo (chromosome aberration, micronucleus, and mouse lymphoma Tk+/- assays). This dataset was then evaluated to assess genotoxic potential using a categorization scheme, whereby a substance was considered genotoxic if it was positive in at least one Ames or clastogen study. The categorization dataset comprised 8442 chemicals, of which 2728 chemicals were genotoxic, 5585 were not and 129 were inconclusive. QSAR models (TEST and VEGA) and the OECD Toolbox structural alerts/profilers (e.g., OASIS DNA alerts for Ames and chromosomal aberrations) were used to make in silico predictions of genotoxicity potential. The performance of the individual QSAR tools and structural alerts resulted in balanced accuracies of 57-73%. A Naïve Bayes consensus model was developed using combinations of QSAR models and structural alert predictions. The ‘best’ consensus model selected had a balanced accuracy of 81.2%, a sensitivity of 87.24% and a specificity of 75.20%. This in silico scheme offers promise as a first step in ranking thousands of substances as part of a prioritization approach for genotoxicity. This dataset is associated with the following publication: Pradeep, P., R. Judson, D. DeMarini, N. Keshava, T. Martin, J. Dean, C. Gibbons, A. Simha, S. Warren, M. Gwinn, and G. Patlewicz. An Evaluation of Existing QSAR Models and Structural Alerts and Development of New Ensemble Models for Genotoxicity Using a Newly Compiled Experimental Dataset. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 18: 100167, (2021).

We have performed high-throughput experimental estimates of five physicochemical properties for a set of 200 chemicals to evaluate the consistency with previous measurements, factors impacting consistency and experimental success, and the applicability domain of the new data in relation to previously measured data and predictive models. This dataset is associated with the following publication: Nicolas, C., K. Mansouri, K. Phillips, C. Grulke, A. Richard, A. Williams, J. Rabinowitz, K. Isaacs, A. Yau, and J. Wambaugh. (ENVIRONMENTAL SCIENCE and TECHNOLOGY) Rapid Experimental Estimates of Physicochemical Properties to Inform Models and Testing. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 636: 901-909, (2018).