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.