This paper describes a model to take chemical structures and predict a property (the point of departure) for a new chemical. No new data were generated. The contents of this zip file contains metadata that you could use to make a model prediction. It does contain all of the code and a help file describing how to run the model. This dataset is associated with the following publication: Pradeep, P., K. Paul-Friedman, and R. Judson. Structure-based QSAR Models to Predict Repeat Dose Toxicity Points of Departure. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 16(November 2020): 100139, (2020).

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).

As described in the README.md file, the GitHub repository github.com/USEPA/PRTR-QSTR-models/tree/data-driven are Python scripts written to run Quantitative Structure–Transfer Relationship (QSTR) models based on chemical structure-based machine learning (ML) models for supporting environmental regulatory decision-making. Using features associated with annual chemical transfer amounts, chemical generator industry sectors, environmental policy stringency, gross value added by industry sectors, chemical descriptors, and chemical unit prices, as in the GitHub repository PRTR_transfers, the QSTR models developed here can predict potential EoL activities for chemicals transferred to off-site locations for EoL management. Also, this contribution shows that QSTR models aid in estimating the mass fraction allocation of chemicals of concern transferred off-site for EoL activities. Also, it describes the Python libraries required for running the code, how to use it, the obtained outputs files after running the Python script, and how to obtain all manuscript figures and results. This dataset is associated with the following publication: Hernandez-Betancur, J.D., G.J. Ruiz-Mercado, and M. Martín. Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models. ACS Sustainable Chemistry & Engineering. American Chemical Society, Washington, DC, USA, 11(9): 3594-3602, (2023).

As described in the README.md file, the GitHub repository github.com/USEPA/PRTR-QSTR-models/tree/data-driven are Python scripts written to run Quantitative Structure–Transfer Relationship (QSTR) models based on chemical structure-based machine learning (ML) models for supporting environmental regulatory decision-making. Using features associated with annual chemical transfer amounts, chemical generator industry sectors, environmental policy stringency, gross value added by industry sectors, chemical descriptors, and chemical unit prices, as in the GitHub repository PRTR_transfers, the QSTR models developed here can predict potential EoL activities for chemicals transferred to off-site locations for EoL management. Also, this contribution shows that QSTR models aid in estimating the mass fraction allocation of chemicals of concern transferred off-site for EoL activities. Also, it describes the Python libraries required for running the code, how to use it, the obtained outputs files after running the Python script, and how to obtain all manuscript figures and results. This dataset is associated with the following publication: Hernandez-Betancur, J.D., G.J. Ruiz-Mercado, and M. Martín. Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models. ACS Sustainable Chemistry & Engineering. American Chemical Society, Washington, DC, USA, 11(9): 3594-3602, (2023).

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).

The data used in this analysis was obtained from published literature and available through the high-throughput toxicokinetic (HTTK) R package. The dataset consists of 1486 chemicals that span a variety of use classes including pharmaceuticals, food-use chemicals, pesticides and industrial chemicals of which 1139 chemicals had experimental human in vitro fraction unbound data and 642 chemicals that had experimental human in vitro intrinsic clearance data. Structures were curated and obtained from the DSSTox database. The distribution of experimental values for fraction unbound and intrinsic clearance is shown in Supplementary Figure S1. Since the data were non-normally distributed they were appropriately transformed before any analysis was conducted. The details of the transformation and the transformed data distribution are presented in the results section and Supplementary Figures S2 and S3. A complete list of chemicals with CAS registry numbers (CASRN), DSSTox generic substance IDs (DTXSIDs), structure and experimental data for both parameters are included as supplemental data (1.ChemicalListData.csv and 1.ChemicalList-QSARready.sdf). This dataset is associated with the following publication: Pradeep, P., G. Patlewicz, R. Pearce, J. Wambaugh, B. Wetmore, and R. Judson. Using Chemical Structure Information to Develop Predictive Models for In Vitro Toxicokinetic Parameters to Inform High-throughput Risk-assessment. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 16: 100136, (2020).

The data used in this analysis was obtained from published literature and available through the high-throughput toxicokinetic (HTTK) R package. The dataset consists of 1486 chemicals that span a variety of use classes including pharmaceuticals, food-use chemicals, pesticides and industrial chemicals of which 1139 chemicals had experimental human in vitro fraction unbound data and 642 chemicals that had experimental human in vitro intrinsic clearance data. Structures were curated and obtained from the DSSTox database. The distribution of experimental values for fraction unbound and intrinsic clearance is shown in Supplementary Figure S1. Since the data were non-normally distributed they were appropriately transformed before any analysis was conducted. The details of the transformation and the transformed data distribution are presented in the results section and Supplementary Figures S2 and S3. A complete list of chemicals with CAS registry numbers (CASRN), DSSTox generic substance IDs (DTXSIDs), structure and experimental data for both parameters are included as supplemental data (1.ChemicalListData.csv and 1.ChemicalList-QSARready.sdf). This dataset is associated with the following publication: Pradeep, P., G. Patlewicz, R. Pearce, J. Wambaugh, B. Wetmore, and R. Judson. Using Chemical Structure Information to Develop Predictive Models for In Vitro Toxicokinetic Parameters to Inform High-throughput Risk-assessment. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 16: 100136, (2020).

Per Imran Shah, this was the Data used in and published as supplemental material for this manuscript. Table S1. Aggregated point of departure (POD) data obtained from ToxRefDB v2.0. Table S2. Chemical structure descriptor data from DSSTox. Table S3. Chemical cluster membership. Table S5. GenRA optimal predictions for each endpoint category and cluster.

Per Imran Shah, this was the Data used in and published as supplemental material for this manuscript. Table S1. Aggregated point of departure (POD) data obtained from ToxRefDB v2.0. Table S2. Chemical structure descriptor data from DSSTox. Table S3. Chemical cluster membership. Table S5. GenRA optimal predictions for each endpoint category and cluster.