Improving the prediction of organism-level toxicity through integration of chemical, protein target and cytotoxicity qHTS data† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c5tx00406c

Using three descriptor domains – encoding complementary bioactivity data – enhances the predictive power, applicability, and interpretability of rat acute-toxicity classifiers.

[1]  M. Suematsu,et al.  Ischemic bowel necrosis induced by endothelin-1: an experimental model in rats. , 1991, Digestion.

[2]  Y. Sawada,et al.  Neurotoxic convulsions induced by histamine H2 receptor antagonists in mice. , 1996, Toxicology and applied pharmacology.

[3]  E. Weitzberg,et al.  The endothelin system in septic and endotoxin shock. , 2000, European journal of pharmacology.

[4]  Paola Gramatica,et al.  The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models , 2003 .

[5]  Andreas Bender,et al.  Molecular Similarity Searching Using Atom Environments, Information-Based Feature Selection, and a Naïve Bayesian Classifier , 2004, J. Chem. Inf. Model..

[6]  Andreas Bender,et al.  Similarity Searching of Chemical Databases Using Atom Environment Descriptors (MOLPRINT 2D): Evaluation of Performance , 2004, J. Chem. Inf. Model..

[7]  P. Berna,et al.  Analysis of the inflammatory response induced by rhMMP-12 catalytic domain instilled in mouse airways. , 2005, International immunopharmacology.

[8]  Adam Yasgar,et al.  Quantitative high-throughput screening: a titration-based approach that efficiently identifies biological activities in large chemical libraries. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Yi Sun.  E3 ubiquitin ligases as cancer targets and biomarkers. , 2006, Neoplasia.

[10]  Douglas M. Hawkins,et al.  Combining Chemodescriptors and Biodescriptors in Quantitative Structure-Activity Relationship Modeling , 2006, J. Chem. Inf. Model..

[11]  Andrew Worth,et al.  Metabolism: A Bottleneck in In Vitro Toxicological Test Development , 2006, Alternatives to laboratory animals : ATLA.

[12]  J. Pernow,et al.  The importance of endothelin-1 for vascular dysfunction in cardiovascular disease. , 2007, Cardiovascular research.

[13]  D. Dix,et al.  The ToxCast program for prioritizing toxicity testing of environmental chemicals. , 2007, Toxicological sciences : an official journal of the Society of Toxicology.

[14]  Andreas Bender,et al.  Ligand-Target Prediction Using Winnow and Naive Bayesian Algorithms and the Implications of Overall Performance Statistics , 2008, J. Chem. Inf. Model..

[15]  Robert J Kavlock,et al.  Understanding mechanisms of toxicity: insights from drug discovery research. , 2008, Toxicology and applied pharmacology.

[16]  Ivan Rusyn,et al.  The Use of Cell Viability Assay Data Improves the Prediction Accuracy of Conventional Quantitative Structure Activity Relationship Models of Animal Carcinogenicity , 2007 .

[17]  Ruili Huang,et al.  Compound Cytotoxicity Profiling Using Quantitative High-Throughput Screening , 2007, Environmental health perspectives.

[18]  A. Shapiro,et al.  Plasminogen activator inhibitor type 1 deficiency , 2008, Haemophilia : the official journal of the World Federation of Hemophilia.

[19]  F. Collins,et al.  Transforming Environmental Health Protection , 2008, Science.

[20]  Alexander Tropsha,et al.  Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure. , 2009, Chemical research in toxicology.

[21]  David M. Reif,et al.  Profiling Chemicals Based on Chronic Toxicity Results from the U.S. EPA ToxRef Database , 2008, Environmental health perspectives.

[22]  Michael P. Wilson,et al.  New Science for Chemicals Policy , 2009, Science.

[23]  Melvin E Andersen,et al.  Toxicity Testing in the 21st Century: Implications for Human Health Risk Assessment , 2009, Risk analysis : an official publication of the Society for Risk Analysis.

[24]  Melvin E Andersen,et al.  Toxicity testing in the 21st century: bringing the vision to life. , 2009, Toxicological sciences : an official journal of the Society of Toxicology.

[25]  Rosa García Couto.  Globally Harmonized System of Classification and Labelling of Chemicals (GHS) , 2009 .

[26]  Subash C. Gupta,et al.  Heat shock proteins in toxicology: how close and how far? , 2010, Life sciences.

[27]  I. Rusyn,et al.  Computational Toxicology: Realizing the Promise of the Toxicity Testing in the 21st Century , 2010, Environmental health perspectives.

[28]  V Rogiers,et al.  Human health safety evaluation of cosmetics in the EU: a legally imposed challenge to science. , 2010, Toxicology and applied pharmacology.

[29]  Chris Morley,et al.  Open Babel: An open chemical toolbox , 2011, J. Cheminformatics.

[30]  Andreas Bender,et al.  From in silico target prediction to multi-target drug design: current databases, methods and applications. , 2011, Journal of proteomics.

[31]  I. Rusyn,et al.  Use of in Vitro HTS-Derived Concentration–Response Data as Biological Descriptors Improves the Accuracy of QSAR Models of in Vivo Toxicity , 2010, Environmental health perspectives.

[32]  Andreas Bender,et al.  The challenges involved in modeling toxicity data in silico: a review. , 2012, Current pharmaceutical design.

[33]  Ruili Huang,et al.  Paradigm Shift in Toxicity Testing and Modeling , 2012, The AAPS Journal.

[34]  Evan Bolton,et al.  PubChem's BioAssay Database , 2011, Nucleic Acids Res..

[35]  Igor I. Baskin,et al.  Machine Learning Methods for Property Prediction in Chemoinformatics: Quo Vadis? , 2012, J. Chem. Inf. Model..

[36]  John P. Overington,et al.  ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..

[37]  Andreas Bender,et al.  In Silico Target Predictions: Defining a Benchmarking Data Set and Comparison of Performance of the Multiclass Naïve Bayes and Parzen-Rosenblatt Window , 2013, J. Chem. Inf. Model..

[38]  E. Skrzypczak‐Jankun,et al.  Plasminogen activator inhibitor-1 in kidney pathology (Review). , 2013, International journal of molecular medicine.

[39]  R. Glen,et al.  Experimental validation of in silico target predictions on synergistic protein targets , 2013 .

[40]  R. Glen,et al.  Diversity Selection of Compounds Based on ‘Protein Affinity Fingerprints’ Improves Sampling of Bioactive Chemical Space , 2013, Chemical biology & drug design.

[41]  Manuel C. Peitsch,et al.  Systems Toxicology: From Basic Research to Risk Assessment , 2014, Chemical research in toxicology.

[42]  J. Dearden,et al.  QSAR modeling: where have you been? Where are you going to? , 2014, Journal of medicinal chemistry.

[43]  Heather C. Dantzker,et al.  Building a Robust 21st Century Chemical Testing Program at the U.S. Environmental Protection Agency: Recommendations for Strengthening Scientific Engagement , 2014, Environmental health perspectives.

[44]  Kun Huang,et al.  Injury, Large-Scale Collaborative Research Program , 2015 .

[45]  Robert P. Sheridan,et al.  eCounterscreening: Using QSAR Predictions to Prioritize Testing for Off-Target Activities and Setting the Balance between Benefit and Risk , 2015, J. Chem. Inf. Model..

[46]  綾子 武見,et al.  Globally Harmonized System of Classification and Labelling of Chemicals (GHS) , 2019, Globally Harmonized System of Classification and Labelling of Chemicals (GHS).