• Rathman J, Yang C, Ribeiro VJ, et al. “Development of a Battery of In Silico Prediction Tools for Drug-Induced Liver Injury from the Vantage Point of Translational Safety Assessment.” Chemical Research in Toxicology. 2021;34(2):601-615.

  • Yang C, Rathman JF, Magdziarz T, Mostrag A, Kulkarni S, Barton-Maclaren TS. “Do Similar Structures Have Similar No Observed Adverse Effect Level (NOAEL) Values? Exploring Chemoinformatics Approaches for Estimating NOAEL Bounds and Uncertainties.” Chemical Research in Toxicology. 2021;34(2):616-633.

  • Firman JW, Pestana CB, Rathman JF, Vinken M, Yang C, Cronin MTD. “A Robust, Mechanistically Based In Silico Structural Profiler for Hepatic Cholestasis.” Chemical Research in Toxicology. 2021;34(2):641-655.

  • Richard AM, Huang R, Waidyanatha S, et al. “The Tox21 10K Compound Library: Collaborative Chemistry Advancing Toxicology.” Chemical Research in Toxicology. 2021;34(2):189-216.


  • Yang C, Cheeseman M, Rathman J, et al. “A new paradigm in threshold of toxicological concern based on chemoinformatics analysis of a highly curated database enriched with antimicrobials.” Food and Chemical Toxicology. 2020;143:111561.

  • Benigni R, Serafimova R, Parra Morte JM, et al. “Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across: An EFSA funded project.” Regulatory Toxicology and Pharmacology. 2020;114:104658.

  • BENFENATI EMILIO, Carnesecchi E, Roncaglioni A, et al. “Maintenance, update and further development of EFSA’s Chemical Hazards: OpenFoodTox 2.0.” EFSA Supporting Publications. 2020;17(3).


  • Cronin MTD, Madden JC, Yang C, Worth AP. “Unlocking the potential of in silico chemical safety assessment – A report on a cross-sector symposium on current opportunities and future challenges.” Computational Toxicology. 2019;10:38-43.

  • Benigni R, Laura Battistelli C, BOSSA CECILIA, et al. “Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across.” EFSA Supporting Publications. 2019;(378).


  • Honma M, Kitazawa A, Cayley A, et al. “Improvement of quantitative structure–activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project.” Mutagenesis. 2018;34(1):3-16.

  • Rathman JF, Yang C, Zhou H. “Dempster-Shafer theory for combining in silico evidence and estimating uncertainty in chemical risk assessment.” Computational Toxicology. 2018;6:16-31.


  • Yang C, Barlow SM, Muldoon Jacobs KL, et al. “Thresholds of Toxicological Concern for cosmetics-related substances: New database, thresholds, and enrichment of chemical space.” Food and Chemical Toxicology. 2017.

  • Boobis A, Brown P, Cronin MTD, et al. “Origin of the TTC values for compounds that are genotoxic and/or carcinogenic and an approach for their re-evaluation.” Critical Reviews in Toxicology. 2017:1-23.


  • Richard AM, Judson RS, Houck KA, et al. “ToxCast Chemical Landscape: Paving the Road to 21st Century Toxicology.” Chemical Research in Toxicology. 2016;29(8):1225-1251.

  • Williams FM, Rothe H, Barrett G, et al. “Assessing the safety of cosmetic chemicals: Consideration of a flux decision tree to predict dermally delivered systemic dose for comparison with oral TTC (Threshold of Toxicological Concern).” Regulatory Toxicology and Pharmacology. 2016;76:174-186.


  • Yang C, Tarkhov A, Marusczyk J, et al. “New Publicly Available Chemical Query Language, CSRML, To Support Chemotype Representations for Application to Data Mining and Modeling.” J. Chem. Inf. Model.. 2015;55(3):510-528. Abstract
    • CSRML



  • Cases M, Briggs K, Steger-Hartmann T, et al. “The eTOX Data-Sharing Project to Advance in Silico Drug-Induced Toxicity Prediction.” International Journal of Molecular Sciences. 2014;15(11):21136-21154. Abstract
    • eTOX project


  • Liu M, Bienfait B, Sacher O, et al. “Combining Chemoinformatics with Bioinformatics: In Silico Prediction of Bacterial Flavor-Forming Pathways by a Chemical Systems Biology Approach “Reverse Pathway Engineering”.” PLoS ONE. 2014;9(1):e84769. Abstract
    • BioPath

  • Vitcheva V, Sharif MA, Tsakovska I, et al. “Description of the MoA/AOP linked with PPARgamma receptor dysregulation leading to liver fibrosis.” Toxicology Letters. 2014;229:S49.

  • Cherkasov A, Muratov EN, Fourches D, et al. “QSAR Modeling: Where Have You Been? Where Are You Going To?” Journal of Medicinal Chemistry. 2014;57(12):4977-5010. Abstract


  • Yang C, Ambrosio M, Arvidson K, et al. “Development of new COSMOS oRepeatDose and non-cancer Threshold of Toxicological Concern (TTC) databases to support alternative testing methods for cosmetics related chemicals.” Toxicology Letters. 2013;221:S80.

  • Richarz A-N, Enoch S, Hewitt M, et al. “In silico workflows for toxicity prediction implemented into KNIME.” Toxicology Letters. 2013;221:S81.

  • Bienfait B, Ertl P. “JSME: a free molecule editor in JavaScript.” Journal of Cheminformatics. 2013;5(1):24. Abstract

  • Williams FM, Ambrosio M, Barrett G, et al. “Threshold of toxicological concern (TTC) task force: a strategy to support application of TTC to dermally applied cosmetic ingredients.” Toxicology Letters. 2013;221:S35.


  • Leist M, Lidbury BA, Yang C, et al. “Novel technologies and an overall strategy to allow hazard assessment and risk prediction of chemicals, cosmetics, and drugs with animal-free methods.” ALTEX. 2012;29(4):373-388. Abstract

  • Hardy B, Apic G, Carthew P, et al. “Toxicology ontology perspectives.” ALTEX. 2012;29(2):139-156. Abstract

  • Briggs K, Cases M, Heard DJ, et al. “Inroads to Predict in Vivo Toxicology-An Introduction to the eTOX Project.” Int. J. Mol. Sci.. 2012;13(3). Abstract

  • Anzali S, Berthold MR, Fioravanzo E, et al. “Development of computational models for the risk assessment of cosmetic ingredients.” IFSCC Magazine. 2012;15:249-255. Abstract


  • Piparo EL, Worth A, Manibusan M, et al. “Use of computational tools in the field of food safety.” Regulatory Toxicology and Pharmacology. 2011;60(3):354-362. Abstract


  • Schwab CH. “Conformations and 3D pharmacophore searching.” Drug Discovery Today: Technologies. 2010;7(4):e245-e253. Abstract

  • Hu X, Yan A, Tan T, Sacher O, Gasteiger J. “Similarity Perception of Reactions Catalyzed by Oxidoreductases and Hydrolases Using Different Classification Methods.” J. Chem. Inf. Model.. 2010;50(6):1089-1100.
    • BioPath, SONNIA

  • Valerio LG, Yang C, Arvidson KB, Kruhlak NL. “A structural feature-based computational approach for toxicology predictions.” Expert Opinion on Drug Metabolism & Toxicology. 2010;6(4):505-518.

  • Schuster D, Kern L, Hristozov D, et al. “Applications of Integrated Data Mining Methods to Exploring Natural Product Space for Acetylcholinesterase Inhibitors.” Comb. Chem. & HTS. 2010;13(1):54-66. Abstract


  • Zaliani A, Boda K, Seidel T, et al. “Second-generation de novo design: a view from a medicinal chemist perspective.” J. Comput. Aided Mol. Des.. 2009;23(8):593-602. Abstract
    • SYLVIA

  • Sacher O, Reitz M, Gasteiger J. “Investigations of Enzyme-Catalyzed Reactions Based on Physicochemical Descriptors Applied to Hydrolases.” J. Chem. Inf. Model.. 2009;49:1525-1534. Abstract
    • BioPath

  • Michielan L, Stephanie F, Terfloth L, et al. “Exploring Potency and Selectivity Receptor Antagonist Profiles Using a Multilabel Classification Approach: The Human Adenosine Receptors as a Key Study.” J. Chem. Inf. Model.. 2009;49(12):2820-2836. Abstract

  • Michielan L, Terfloth L, Gasteiger J, Moro S. “Comparison of Multilabel and Single-Label Classification Applied to the Prediction of the Isoform Specificity of Cytochrome P450 Substrates.” J. Chem. Inf. Model.. 2009;49(11):2588-2605. Abstract

  • Kornhuber J, Terfloth L, Bleich S, Wiltfang J, Rupprecht R. “Molecular properties of psychopharmacological drugs determining non-competitive inhibition of 5-HT3A receptors.” Eur. J. Med. Chem.. 2009;44(6):2667-2672. Abstract
    • ADRIANA.Code

  • Yang C, Valerio, Luis G. J, Arvidson KB. “Computational toxicology approaches at the US Food and Drug Administration.” Altern Lab Anim.. 2009;37(5):523-531. Abstract


  • Kastenmueller G, Gasteiger J, Mewes H-W. “An environmental perspective on large-scale genome clustering based on metabolic capabilities.” Bioinformatics. 2008;24:i56-62. Abstract
    • BioPath

  • Wagner S, Arce R, Murillo R, Terfloth L, Gasteiger J, Merfort I. “Neural Networks as Valuable Tools To Differentiate between Sesquiterpene Lactones’ Inhibitory Activity on Serotonin Release and on NF-κB.” J. Med. Chem.. 2008;51(5):1324-1332. Abstract


  • Matthews EJ, Kruhlak NL, Benz DR, Contrera JF, Marchant CA, Yang C. “Combined Use of MC4PC, MDL-QSAR, BioEpisteme, Leadscope PDM, and Derek for Windows Software to Achieve High-Performance, High-Confidence, Mode of Action–Based Predictions of Chemical Carcinogenesis in Rodents.” Toxicology Mechanisms and Methods. 2008;18(2-3):189-206. Abstract

  • Kornhuber J, Tripal P, Reichel M, et al. “Identification of New Functional Inhibitors of Acid Sphingomyelinase Using a Structure−Property−Activity Relation Model.” J. Med. Chem.. 2008;51(2):219-237. Abstract

  • Richard AM, Yang C, Judson RS. “Toxicity Data Informatics: Supporting a New Paradigm for Toxicity Prediction.” Toxicology Mechanisms and Methods. 2008;18(2-3):103-118.

  • Yang C, Hasselgren CH, Boyer S, et al. “Understanding Genetic Toxicity Through Data Mining: The Process of Building Knowledge by Integrating Multiple Genetic Toxicity Databases.” Toxicology Mechanisms and Methods. 2008;18(2-3):277-295.


  • Terfloth L, Bienfait B, Gasteiger J. “Ligand-Based Models for the Isoform Specificity of Cytochrome P450 3A4, 2D6, and 2C9 Substrates.” J. Chem. Inf. Model.. 2007;47(4):1688-1701. Abstract
    • isoCYP

  • Boda K, Seidel T, Gasteiger J. “Structure and reaction based evaluation of synthetic accessibility.” Journal of Computer-Aided Molecular Design. 2007;21(6):311-325.

    • SYLVIA

  • Kaiser D, Terfloth L, Kopp S, et al. “Self-Organizing Maps for Identification of New Inhibitors of P-Glycoprotein.” J. Med. Chem.. 2007;50(7):1698-1702. Abstract


  • Benigni R, NETZEVA TATIANAI, BENFENATI EMILIO, et al. “The Expanding Role of Predictive Toxicology: An Update on the (Q)SAR Models for Mutagens and Carcinogens.” Journal of Environmental Science and Health, Part C. 2007;25(1):53-97.


  • Renner S, Schwab CH, Gasteiger J, Schneider G. “Impact of Conformational Flexibility on Three-Dimensional Similarity Searching Using Correlation Vectors.” Journal of Chemical Information and Modeling. 2006;46(6):2324-2332.

  • Moro S, Spalluto G, Cusan C, et al. “The application of a 3D-QSAR (autoMEP/PLS) approach as an efficient pharmacodynamic-driven filtering method for small-sized virtual library: Application to a lead optimization of a human A3 adenosine receptor antagonist.” Bioorg. Med. Chem.. 2006;14(14):4923-4932. Abstract

    • ADRIANA.Code

  • Wagner S, Hofmann A, Siedle B, Terfloth L, Merfort I, Gasteiger J. “Development of a Structural Model for NF-κB Inhibition of Sesquiterpene Lactones Using Self-Organizing Neural Networks.” J. Med. Chem.. 2006;49(7):2241-2252. Abstract


  • Blower P, Cross K, Eichler G, Myatt G, Weinstein J, Yang C. “Comparison of Methods for Sequential Screening of Large Compound Sets.” Combinatorial Chemistry & High Throughput Screening. 2006;9(2):115-122. Abstract


  • Moro S, Bacilieri M, Cacciari B, Spalluto G. “Autocorrelation of Molecular Electrostatic Potential Surface Properties Combined with Partial Least Squares Analysis as New Strategy for the Prediction of the Activity of Human A3 Adenosine Receptor Antagonists.” J. Med. Chem.. 2005;48:5698-5704. Abstract
    • ADRIANA.Code

  • Moro S, Bacilieri M, Ferrari C, Spalluto G. “Autocorrelation of molecular electrostatic potential surface properties combined with partial least squares analysis as alternative attractive tool to generate ligand-based 3D-QSARs.” Curr. Drug Disc. Tech.. 2005;2:13-21. Abstract

    • ADRIANA.Code

  • Da Costa FB, Terfloth L, Gasteiger J. “Sesquiterpene lactone-based classification of three Asteraceae tribes: a study based on self-organizing neural networks applied to chemosystematics.” Phytochemistry. 2005;66(3):345-353. Abstract


  • Reitz M, Sacher O, Tarkhov A, Trümbach D, Gasteiger J. “Enabling the exploration of biochemical pathways.” Organic & Biomolecular Chemistry. 2004;2(22):3226-3237.
    • BioPath

  • Teckentrup A, Briem H, Gasteiger J. “Mining High-Throughput Screening Data of Combinatorial Libraries: Development of a Filter to Distinguish Hits from Nonhits.” J. Chem. Inf. Model.. 2004;44(2):626-634. Abstract



  • Gasteiger J, Teckentrup A, Terfloth L, Spycher S. “Neural networks as data mining tools in drug design.” J. Phys. Org. Chem.. 2003;16(4):232-245. Abstract

  • Gasteiger J, Engel T. Chemoinformatics: A Textbook. Wiley-VCH; 2003.

  • Gasteiger J. Handbook of Chemoinformatics. From Data to Knowledge. Wiley-VCH; 2003.

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