Computational Toxicology

This dataset include glutathione reactivity (RC50) and toxicity values to Tetrahymena pyriformis (IGC50) for 29 SN2 compounds activated by a carbonyl electron-withdrawing group. Calculated activation energy values (Eact) and predictions for both glutathione reactivity and toxicity to Tetrahymena pyriformis using these values are also included.

This data set includes in vivo pulmonary toxicity response information from exposures to metal oxide nanoparticles curated from published peer reviewed literature.

More elaborate description of specific methods (word file) and the used dataset (excel file).

This file pertains 1) all SMILES(except the evaluation set-3 which contains the compound data from in-house proprietary projects) with respective pIC50 values that were used in training and evaluating the models 2) list of descriptors that were used to build models

A dataset of 356 compounds, mainly drugs or drug candidates, which consisted of groups of congeneric series sharing a common scaffold. The collected “LXR binders” covered a wide range of binding affinity, with IC50 values spanning from 1 nM to greater than 10000 nM. The dataset of LXR binders was enriched with decoy molecules, i.e. molecules that are presumed to be inactive against a target (they will not likely bind to the target). Decoys are commonly used to validate the performance of molecular modelling studies, as for example molecular docking, which was used in the present work. One-thousand decoy molecules were selected from Schrodinger 1K Drug-Like Ligand Decoys Set. For these molecules results obtained from the following modelling approaches are reported: ensemble docking, ePharmacophore, fingerprint similarity, structural alerts and QSAR PLS model. These results were used to build ranking strategies proposed in the paper.

The dataset that was evaluated in this approach was taken from Wambaugh et al [29] who filtered the Tox21 library to reflect substances with similar uses to those in NHANES. The zip file contains the supplementary information being provided for the re-analysis performed in this dataset. There was no specific code as such developed for the analysis aside from using KNIME to help combine different outputs from different tools including Leadscope in order to arrive at the counts reflected in Table 2 of the manuscript. Instead of this very laborious approach, we re-did the analysis using Toxtree alone and streamlined the processing of the outcomes with R. This is documented in the supplementary information file. List of files: SMARTS Toxtree schemes use to identify carbamates, OPs and steroids Carbamates.tml OPs.tml Steroids.tml R code used to manipulate the various outputs derived from processing the associated sdf through the Kroes, specific Toxtree schemes and Cramer scheme within Toxtree TTC_HTTK.R R data file HTTK_TTC_070218.RData sdf file used in the analysis HTTK_7K_mod_kekule.sdf

Datasets used in ORD-023408: A Gene Expression Biomarker Identifies Chemicals and Other Factors That Modulate Sterol Regulatory Element Binding Protein (SREBP) Highlighting Differences in Targeted Regulation of Cholesterogenic and Lipogenic Genes

We present a novel use of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene associations with biological concepts as represented by Medical Subject Headings (MeSH terms) in PubMed.

Datasets used in ORD-023413: Frequent Modulation of the Sterol Regulatory Element Binding Protein (SREBP) by Chemical Exposure in the Livers of Rats

These new analysis builds on the baseline GenRA approach and presents a proof of concept of how other contexts of similarity namely physchem can be implemented into a search strategy for identification of analogues and how this impacts performance of read-across. Chemicals Involved: Same ToxRef dataset as used in the original GenRA manuscript.