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.

Here are all of the data files used for this manuscript. Please note that this is all published data. Imran Shah 1.1060+ Chemicals and Chemical controls 2. Chemical descriptors (chm): 2048 Morgan (mrgn) 2048 Topological Torsion (tptr) 729 ToxPrints (toxp) 3. Transcriptomic descriptors(bio): 95 Gene (ge) 189 Assay (asy) 4. 922 Toxicity outcomes(tox) 5. 86 Predefined Chemical Clusters

Here are all of the data files used for this manuscript. Please note that this is all published data. Imran Shah 1.1060+ Chemicals and Chemical controls 2. Chemical descriptors (chm): 2048 Morgan (mrgn) 2048 Topological Torsion (tptr) 729 ToxPrints (toxp) 3. Transcriptomic descriptors(bio): 95 Gene (ge) 189 Assay (asy) 4. 922 Toxicity outcomes(tox) 5. 86 Predefined Chemical Clusters

This repository contains code, input and output files associated with the GenRA acute toxicity case study that was published by Helman et al (2019) in Computational Toxicology. This dataset is associated with the following publication: Helman, G., I. Shah, and G. Patlewicz. Transitioning the Generalised Read-Across approach (GenRA) to quantitative predictions: A case study using acute oral toxicity data. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 12(November 2019): 100097, (2019).

This repository contains code, input and output files associated with the GenRA acute toxicity case study that was published by Helman et al (2019) in Computational Toxicology. This dataset is associated with the following publication: Helman, G., I. Shah, and G. Patlewicz. Transitioning the Generalised Read-Across approach (GenRA) to quantitative predictions: A case study using acute oral toxicity data. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 12(November 2019): 100097, (2019).

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).

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).

This dataset comprises one SigmaPlot 13 file containing measured survival data and survival data predicted from the model coefficients selected by the LASSO procedure. This dataset is associated with the following publication: Ross, J., B. George, M. Bruno, and Y. Ge. Chemical-agnostic hazard prediction: statistical inference of in vitro toxicity pathways from proteomics responses to chemical mixtures. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 2: 39-44, (2017).

This dataset comprises one SigmaPlot 13 file containing measured survival data and survival data predicted from the model coefficients selected by the LASSO procedure. This dataset is associated with the following publication: Ross, J., B. George, M. Bruno, and Y. Ge. Chemical-agnostic hazard prediction: statistical inference of in vitro toxicity pathways from proteomics responses to chemical mixtures. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 2: 39-44, (2017).

In an effort to address a major challenge in chemical safety assessment, alternative approaches for characterizing systemic effect levels, a predictive model was developed. Systemic effect levels were curated from ToxRefDB, HESS-DB and COSMOS-DB from numerous study types totaling 4382 in vivo studies for 1201 chemicals. Observed systemic effects in mammalian models are a complex function of chemical dynamics, kinetics, and inter- and intra-individual variability. In order to address the complexity problem, systemic effect levels were modeled at the study-level by leveraging study covariates (e.g., study type, strain, administration route) in addition to multiple descriptor sets, including chemical (ToxPrint, PaDEL, and Physchem), biological (ToxCast), and kinetic descriptors. Using Random Forest modeling with cross-validation and external validation procedures, study-level covariates alone accounted for approximately 20% of the variance reducing the root mean squared error (RMSE) from 0.96 log10 mg/kg/day to 0.85 log10 mg/kg/day, providing a baseline performance metric (lower expectation of model performance). A consensus model developed using a combination of study-level covariates, chemical, biological, and kinetic descriptors explained a total of 38% of the variance with an RMSE of 0.76 log10 mg/kg/day. A benchmark model (upper expectation of model performance) was also developed with an RMSE of 0.5 log10 mg/kg/day by incorporating study-level covariates and the mean effect level per chemical. To achieve a representative chemical-level prediction, the minimum study-level predicted and observed effect level per chemical were compared reducing the RMSE from 1.1 to 0.8 log10 mg/kg/day. Although biological descriptors did not improve model performance, the final model was enriched for biological descriptors that indicated xenobiotic metabolism gene expression, oxidative stress, and cytotoxicity, demonstrating the importance of accounting for kinetics and non-specific bioactivity in predicting systemic effect levels. Herein, we have generated an externally predictive model of systemic effect levels for use as a safety assessment tool and have generated forward predictions for thousands of chemicals. This dataset is associated with the following publication: Truong, L., G. Ouedraogo, L. Pham, J. Clouzeau, S. Loisel-Joubert, D. Blanchet, H. Noçairi, W. Setzer, R. Judson, C. Grulke, K. Mansouri, and M. Martin. (Archives of Toxicology) Predicting In Vivo Effect Levels for Repeat Dose Systemic Toxicity using Chemical, Biological, Kinetic and Study Covariates. Archives of Toxicology. Springer, New York, NY, USA, 92(2): 587-600, (2018).