Dataset backup for computational experiments in the paper: "Ranking Molecules with Vanishing Kernels and a Single Parameter: Applicability Domain Included" Berenger, F. and Yamanishi, Y. Data source https://github.com/deepchem/deepchem/tree/master/datasets/tox21.csv download date: 09/11/2018 at 13:59:05 Preparation protocol All smiles strings (molecules) have been standardised using https://github.com/flatkinson/standardiser Molecules that did not pass standardisation have been removed. Cf. standardisation/errors.smi for such molecules. All molecules tested on a given toxicity endpoint/target were copied into a specific directory for that target. All toxic molecules for a given target have had their name prefixed with the word "active". Each list of molecules was randomized. Directory structure tox21.csv: backup copy of the original data source targets.txt: list of all toxicity endpoints in the dataset; one per line. Target names are in the same order than columns in the tox21.csv file. TARGET/ligands_std_rand.smi: all toxic molecules for TARGET and all non toxic molecules; in random order standardisation/errors.smi: molecules that did not pass standardisation standardisation/standardised.smi: molecules that passed standardisation Bibliography @article{Huang2016, author = {Huang, Ruili and Xia, Menghang and Nguyen, Dac-Trung and Zhao, Tongan and Sakamuru, Srilatha and Zhao, Jinghua and Shahane, Sampada A. and Rossoshek, Anna and Simeonov, Anton}, title = {Tox21Challenge to Build Predictive Models of Nuclear Receptor and Stress Response Pathways as Mediated by Exposure to Environmental Chemicals and Drugs}, journal = {Frontiers in Environmental Science}, volume = {3}, pages = {85}, year = {2016}, doi = {10.3389/fenvs.2015.00085}, }

{"references": ["Huang, R., Xia, M., Nguyen, D. T., Zhao, T., Sakamuru, S., Zhao, J., ... & Simeonov, A. (2016). Tox21Challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs. Frontiers in Environmental Science, 3, 85."]}