CERAPP: Collaborative Estrogen Receptor Activity Prediction Project
Open accessAbstract
Background: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. Objectives: We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing. Methods: CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure–activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies. Results: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing. Conclusion: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other end points. Citation: Mansouri K, Abdelaziz A, Rybacka A, Roncaglioni A, Tropsha A, Varnek A, Zakharov A, Worth A, Richard AM, Grulke CM, Trisciuzzi D, Fourches D, Horvath D, Benfenati E, Muratov E, Wedebye EB, Grisoni F, Mangiatordi GF, Incisivo GM, Hong H, Ng HW, Tetko IV, Balabin I, Kancherla J, Shen J, Burton J, Nicklaus M, Cassotti M, Nikolov NG, Nicolotti O, Andersson PL, Zang Q, Politi R, Beger RD, Todeschini R, Huang R, Farag S, Rosenberg SA, Slavov S, Hu X, Judson RS. 2016. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environ Health Perspect 124:1023–1033; http://dx.doi.org/10.1289/ehp.1510267
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Consensus QSAR models from CERAPP and CoMPARA predicted ER and AR potentials of 32,464 new chemicals.
“The resulting consensus QSAR models leveraged knowledge from the groups and were used to predict ER and AR potentials of 32,464 new chemicals.”
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Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works
Self-citationToxCast Chemical Landscape: Paving the Road to 21st Century Toxicology
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Self-citationOPERA models for predicting physicochemical properties and environmental fate endpoints
Mansouri, K., Grulke, C., Judson, R., & Williams, A. J. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0263-1 · Cited by 635 · see citing works
Self-citationToxicity testing in the 21st century: progress in the past decade and future perspectives
Krewski, D. R., Andersen, M. E., Tyshenko, M. G., Krishnan, K., Härtung, T., Boekelheide, K., Wambaugh, J. F., Jones, D. P., Whelan, M., Thomas, R. S., et al. (2019) · Archives of Toxicology · doi:10.1007/s00204-019-02613-4 · Cited by 384 · see citing works
Thomas, R. S., Bahadori, T., Buckley, T. J., Cowden, J., Deisenroth, C., Dionisio, K. L., Frithsen, J. B., Grulke, C., Gwinn, M. R., Harrill, J., et al. (2019) · Toxicological Sciences · doi:10.1093/toxsci/kfz058 · Cited by 386 · see citing works
Self-citationThe Tox21 10K Compound Library: Collaborative Chemistry Advancing Toxicology
Richard, A. M., Huang, R., Waidyanatha, S., Shinn, P., Collins, B. J., Thillainadarajah, I., Grulke, C. M., Williams, A. J., Lougee, R. R., Judson, R. S., et al. (2021) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.0c00264 · Cited by 323 · see citing works
Self-citationTrust, but Verify II: A Practical Guide to Chemogenomics Data Curation
Fourches, D., Muratov, E., & Tropsha, A. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00129 · Cited by 294 · see citing works
Self-citationA framework for establishing scientific confidence in new approach methodologies
van der Zalm, A. J., Barroso, J., Browne, P., Casey, W., Gordon, J., Henry, T. R., Kleinstreuer, N., Lowit, A., Perron, M. M., & Clippinger, A. J. (2022) · Archives of Toxicology · doi:10.1007/s00204-022-03365-4 · Cited by 263 · see citing works
A renaissance of neural networks in drug discovery
Baskin, I. I., Winkler, D., & Tetko, I. V. (2016) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2016.1201262 · Cited by 235 · see citing works
Self-citationIntegrating tools for non-targeted analysis research and chemical safety evaluations at the US EPA
Sobus, J. R., Wambaugh, J. F., Isaacs, K., Williams, A. J., McEachran, A. D., Richard, A. M., Grulke, C., Ulrich, E. M., Rager, J. E., Strynar, M. J., et al. (2017) · Journal of Exposure Science & Environmental Epidemiology · doi:10.1038/s41370-017-0012-y · Cited by 214 · see citing works
Self-citationCoMPARA: Collaborative Modeling Project for Androgen Receptor Activity
Mansouri, K., Kleinstreuer, N., Abdelaziz, A. M., Alberga, D., Alves, V. M., Andersson, P. L., Andrade, C. H., Bai, F., Balabin, I. A., Ballabio, D., et al. (2020) · Environmental Health Perspectives · doi:10.1289/ehp5580 · Cited by 208 · see citing works
Self-citationZang, Q., Mansouri, K., Williams, A. J., Judson, R., Allen, D., Casey, W., & Kleinstreuer, N. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00625 · Cited by 199 · see citing works
Self-citationCritical Assessment of Small Molecule Identification 2016: automated methods
Schymanski, E., Ruttkies, C., Krauss, M. K. V., Brouard, C., Kind, T., Dührkop, K., Allen, F. R., Vaniya, A., Verdegem, D., Böcker, S., et al. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0207-1 · Cited by 177 · see citing works
Filimonov, D. A., Druzhilovskiy, D. S., Lagunin, A. A., Gloriozova, T. A., Rudik, A. V., Dmitriev, A. V., Pogodin, P. V., & Poroikov, V. (2018) · Biomedical Chemistry Research and Methods · doi:10.18097/bmcrm00004 · Cited by 200 · see citing works
Open-source QSAR models for pKa prediction using multiple machine learning approaches
Mansouri, K., Cariello, N. F., Korotcov, A. V., Tkachenko, V., Grulke, C., Sprankle, C. S., Allen, D., Casey, W., Kleinstreuer, N., & Williams, A. J. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0384-1 · Cited by 177 · see citing works
Self-citationComparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction
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Dark matter in host-microbiome metabolomics: Tackling the unknowns–A review
Peisl, B. Y. L., Schymanski, E., & Wilmes, P. (2017) · Analytica Chimica Acta · doi:10.1016/j.aca.2017.12.034 · Cited by 147 · see citing works
CATMoS: Collaborative Acute Toxicity Modeling Suite
Mansouri, K., Karmaus, A. L., Fitzpatrick, J. M., Patlewicz, G. Y., Pradeep, P., Alberga, D., Alépée, N., Allen, T. E. H., Allen, D., Alves, V. M., et al. (2021) · Environmental Health Perspectives · doi:10.1289/ehp8495 · Cited by 147 · see citing works
Self-citationEvaluation of estrogen receptor alpha activation by glyphosate-based herbicide constituents
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Self-citationNavigating through the minefield of read-across tools: A review of in silico tools for grouping
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Identifying known unknowns using the US EPA’s CompTox Chemistry Dashboard
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Structure-Based Prediction of hERG-Related Cardiotoxicity: A Benchmark Study
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Self-citationLigand efficiency metrics in drug discovery: the pros and cons from a practical perspective
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Self-citationReview of machine learning and deep learning models for toxicity prediction
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Self-citationMolecular Descriptors for Structure–Activity Applications: A Hands-On Approach
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Self-citation“MS-Ready” structures for non-targeted high-resolution mass spectrometry screening studies
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Self-citationMolecular dynamics simulations and applications in computational toxicology and nanotoxicology
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Self-citationImpact of Molecular Descriptors on Computational Models
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Self-citationEndocrine disruptors of sex hormone activities
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Self-citationComputer-Aided Ligand Discovery for Estrogen Receptor Alpha
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Evaluation of food-relevant chemicals in the ToxCast high-throughput screening program
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Self-citationAn integrated chemical environment with tools for chemical safety testing
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Self-citationIntegrated Framework for Identifying Toxic Transformation Products in Complex Environmental Mixtures
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Predicting Organ Toxicity Using in Vitro Bioactivity Data and Chemical Structure
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Prediction of Hydrolysis Products of Organic Chemicals under Environmental pH Conditions
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Self-citationA tiered, Bayesian approach to estimating population variability for regulatory decision-making
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In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9
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In Silico Predictions of Endocrine Disruptors Properties
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Application of new approach methodologies: ICE tools to support chemical evaluations
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Developing context appropriate toxicity testing approaches using new alternative methods (NAMs)
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OECD approaches and considerations for regulatory evaluation of endocrine disruptors
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Self-citationConsensus versus Individual QSARs in Classification: Comparison on a Large-Scale Case Study
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Self-citationMachine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction
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Chemical Risk Assessment: Traditional vs Public Health Perspectives
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Chemical representation learning for toxicity prediction
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Protecting Human and Animal Health: The Road from Animal Models to New Approach Methods
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Twenty years of transcriptomics, 17alpha-ethinylestradiol, and fish
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Self-citationUncertainty quantification in ToxCast high throughput screening
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A comprehensive review of regulatory test methods for endocrine adverse health effects
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The predictivity of QSARs for toxicity: Recommendations for improving model performance
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Systematic Green Solvent Selection for the Hydroformylation of Long-Chain Alkenes
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Alofe, O., Kisanga, E. P., Inayat‐Hussain, S. H., Fukumura, M., García-Milian, R., Perera, L., Vasiliou, V., & Whirledge, S. (2019) · Environment International · doi:10.1016/j.envint.2019.104969 · Cited by 38 · see citing works
Cotterill, J., Palazzolo, L., Ridgway, C., Price, N. R., Rorije, E., Moretto, A., Peijnenburg, A., & Eberini, I. (2019) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2019.114630 · Cited by 37 · see citing works
Mining of Consumer Product Ingredient and Purchasing Data to Identify Potential Chemical Coexposures
Stanfield, Z., Addington, C. K., Dionisio, K. L., Lyons, D., Tornero‐Velez, R., Phillips, K. A., Buckley, T. J., & Isaacs, K. (2021) · Environmental Health Perspectives · doi:10.1289/ehp8610 · Cited by 37 · see citing works
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Integration of new approach methods for the assessment of data-poor chemicals
Friedman, K. P., Thomas, R. S., Wambaugh, J. F., Harrill, J., Judson, R., Shafer, T. J., Williams, A. J., Lee, J.-Y. J., Loo, L., Gagné, M., et al. (2025) · Toxicological Sciences · doi:10.1093/toxsci/kfaf019 · Cited by 36 · see citing works
Self-citationMolecular Docking for Predictive Toxicology
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Self-citation
- Authors
- Mansouri, K., Abdelaziz, A., Rybacka, A., Roncaglioni, A., Tropsha, A., Varnek, A., Zakharov, A., Worth, A., Richard, A. M., Grulke, C. M., Trisciuzzi, D., Fourches, D., Horvath, D., Benfenati, E., Muratov, E., Wedebye, E. B., Grisoni, F., Mangiatordi, G. F., Incisivo, G. M., Hong, H., Ng, H. W., Tetko, I. V., Balabin, I., Kancherla, J., Shen, J., Burton, J., Nicklaus, M., Cassotti, M., Nikolov, N. G., Nicolotti, O., Andersson, P. L., Zang, Q., Politi, R., Beger, R. D., Todeschini, R., Huang, R., Farag, S., Rosenberg, S. A., Slavov, S., Hu, X., & Judson, R. S.
- Affiliations
- Environmental Protection Agency (US)
- Helmholtz Munich (DE)
- Umeå University (SE)
- Mario Negri Institute for Pharmacological Research (IT)
- University of North Carolina at Chapel Hill (US)
- Université de Strasbourg (FR)
- National Institutes of Health (US)
- European Commission (BE)
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
- University of Bari Aldo Moro (IT)
- Technical University of Denmark (DK)
- University of Milano-Bicocca (IT)
- National Center for Toxicological Research (US)
- Lockheed Martin (United States) (US)
- Research Institute for Fragrance Materials (US)
- Integrated Laboratory Systems, Inc. (US)
- Corresponding author
- Judson, Richard S.
- ORCID
- Mansouri, Kamel
- Roncaglioni, Alessandra
- Tropsha, Alexander
- Varnek, Alexandre
- Zakharov, Alexey
- Worth, Andrew
- Trisciuzzi, Daniela
- Fourches, Denis
- Horvath, Dragos
- Benfenati, Emilio
- Muratov, Eugene
- Wedebye, Eva Bay
- Grisoni, Francesca
- Mangiatordi, Giuseppe F.
- Hong, Huixiao
- Tetko, Igor V.
- Balabin, Ilya
- Kancherla, Jayaram
- Shen, Jie
- Nicklaus, Marc
- Cassotti, Matteo
- Nikolov, Nikolai G.
- Nicolotti, Orazio
- Andersson, Patrik L.
- Zang, Qingda
- Beger, Richard D.
- Todeschini, Roberto
- Huang, Ruili
- Rosenberg, Sine A.
- Slavov, Svetoslav
- Hu, Xin
- Judson, Richard S.
- Venue
- Environmental Health Perspectives
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-02-23
- Volume
- 124
- Issue
- 7
- Pages
- 1023-1033
- ISSN
- 0091-6765 (ISSN-L)
- 1552-9924
- PMC
- PMC4937869
- OpenAlex
- W2394108223
- Cited by
- 375
- References
- 85
- Citations by year
- 2026: 26 · 2025: 46 · 2024: 35 · 2023: 32 · 2022: 36 · 2021: 41 · 2020: 35 · 2019: 30 · 2018: 38 · 2017: 29 · 2016: 27
- FWCI
- 38.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- creativecommons.org/licenses/by/4.0/ (vor, 675 d)
- Locations
- Environmental Health Perspectives (publishedVersion, public-domain)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion, cc-by)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- HAL (submittedVersion, other-oa)
- BOA (University of Milano-Bicocca) (submittedVersion)
- Site cant be reached (submittedVersion, cc-by)
- Joint Research Centre (European Commission) (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
- CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) (submittedVersion, other-oa)
- Funders
- National Institute of General Medical Sciences (T32GM067553)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Effects and risks of endocrine disrupting chemicals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Estrogen and related hormone effects
- Keywords
- endocrine disruptors, estrogen receptor, QSAR, molecular docking, consensus model, chemical prioritization, high-throughput screening, chemical risk assessment
- MeSH terms
- Computer Simulation, Receptors, Estrogen, United States, Toxicity Tests, Quantitative Structure-Activity Relationship, Endocrine Disruptors, Environmental Policy
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