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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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“The resulting consensus QSAR models leveraged knowledge from the groups and were used to predict ER and AR potentials of 32,464 new chemicals.”
From QSAR without borders (2020) · cites this paper as [89]
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“For example, people using chemicals in the workplace, higher intensity users, children, and some racial/ethnic minorities are expected to have much higher exposures than those at the population median (Arcury et al. 2018; McKelvey et al. 2011; Nguyen et al. 2019, 2020; Trowbridge et al. 2020).”
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“CERAPP predicts that octisalate, homosalate, octocrylene, oxybenzone, octinoxate, and avobenzone were inactive or had weak agonist/antagonist estrogenic activity (Mansouri et al. , 2016), and consistent with CompTox EDSP21 dashboard.”
ResultGrouping strategies for assessing and managing persistent and mobile substances
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“For example, the CERAPP and CoMPARA models in OPERA can be used to predict estrogen and androgen receptor interactions [116, 121].”
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“EPA as a part of the CERAPP project [48] and contains 32,464 unique structures to which humans are potentially exposed.”
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“For example, consensus models have been trained using ToxCast data to make categorical or continuous predictions on a chemical’s potential to interact with endocrine or androgen receptors (Mansouri et al., 2020, 2016).”
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“A KNIME structure-standardization workflow was used to process these lists of chemicals (Mansouri et al., 2016).”
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“In this study, the data used for model development was mostly derived from the data reported by authoritative journals (Judson et al., 2016; Kojima et al., 2003; Sun et al., 2012), which, to some extent, increased the reliability of the input data.”
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“…EDC activity was carried out by the United States Environmental Protection Agency (US EPA), under the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) (Mansouri et al., 2016) and Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) (Mansouri et al., 2020).”
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Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities
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New approach methods for testing chemicals for endocrine disruption potential
Judson, R., Friedman, K. P., Houck, K. A., Mansouri, K., Browne, P., & Kleinstreuer, N. (2018) · Current Opinion in Toxicology · doi:10.1016/j.cotox.2018.10.002 · Cited by 19
Self-citationLevine, S. L., Webb, E. G., & Saltmiras, D. A. (2020) · Pest Management Science · doi:10.1002/ps.5983 · Cited by 19
Tang, W., Chen, J., & Hong, H. (2020) · Chemosphere · doi:10.1016/j.chemosphere.2020.128567 · Cited by 19
Self-citationYang, Z., Wang, L., Duan, Y., Zhang, W., Pang, S., Sun, Y., Liang, Y., & Cao, H. (2024) · Environmental Science & Technology · doi:10.1021/acs.est.3c09779 · Cited by 18
Zhao, Y., Deng, Y., Shen, F., Huang, J., Yang, J., Lu, H., Wang, J., Liang, X., & Su, G. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.135351 · Cited by 19
Hong, H., Shen, J., Ng, H. W., Sakkiah, S., Ye, H., Ge, W., Gong, P., Xiao, W., & Tong, W. (2016) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph13040372 · Cited by 18
Self-citationBeames, T. G., Moreau, M., Roberts, A., Mansouri, K., Haider, S., Smeltz, M. G., Nicolas, C. I., Doheny, D. L., Phillips, M. B., Yoon, M., et al. (2019) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2019.114774 · Cited by 18
Self-citationDu, Y., Tang, T., Song, D., Wang, R., Liu, H., Du, X., Dang, Z., & Lu, G. (2025) · Environmental Pollution · doi:10.1016/j.envpol.2025.125976 · Cited by 19
A novel risk score-based prioritization method for pollutants in reclaimed water
Guo, Q., Wei, D., Wang, F., Chen, M., & Du, Y. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.148833 · Cited by 16
Stossi, F., Singh, P. K., Mistry, R. M., Johnson, H. L., Dandekar, R. D., Mancini, M. G., Szafran, A. T., Rao, A. U. K., & Mancini, M. A. (2022) · Environmental Health Perspectives · doi:10.1289/ehp9297 · Cited by 16
“…e ff orts spearheaded by the U.S. EPA and National Toxicology Program (NTP) ToxCast and Tox21 programs, using a battery of in vitro and in vivo assays (Huang et al. 2014; Judson et al. 2015; Mansouri et al. 2016; Richard et al. 2016; Rotro ff et al. 2014; Stossi et al. 2014; Szafran et al. 2017).”
Phytoestrogens and Mycoestrogens Induce Signature Structure Dynamics Changes on Estrogen Receptor α
Chen, X., Uzuner, U., Li, M., Shi, W., Yuan, J. S., & Dai, S. Y. (2016) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph13090869 · Cited by 17
Löffler, P., Schymanski, E., Henschel, H., & Lai, F. Y. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c06790 · Cited by 17
ProfhEX: AI-based platform for small molecules liability profiling
Lunghini, F., Fava, A., Pisapia, V., Sacco, F. M., Iaconis, D., & Beccari, A. R. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00728-6 · Cited by 17
“The COMPARA and CERAPP collaborative projects [28, 29] are two examples of first tier screening models built on HTS data for androgen and estrogen receptor activity, respectively.”
BackgroundYang, C., Rathman, J. F., Mostrąg, A., Ribeiro, J. V., Hobocienski, B., Magdziarz, T., Kulkarni, S. A., & Barton‐Maclaren, T. S. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.3c00062 · Cited by 16
Fan, F., Wu, G., Yang, Y., Liu, F., Qian, Y., Yu, Q., Ren, H., & Geng, J. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.3c04571 · Cited by 17
Kvasnicka, J., Aurisano, N., von Borries, K. J. F., Lu, E., Fantke, P., Jolliet, O., Wright, F. A., & Chiu, W. A. (2024) · Environmental Science & Technology · doi:10.1021/acs.est.4c00172 · Cited by 18
“For consistency, we preprocessed these features by excluding any chemicals that had been filtered out in the QSAR-standardization workflow by Mansouri et al. 4,5”
MethodIn Silico Study of In Vitro GPCR Assays by QSAR Modeling
Mansouri, K., & Judson, R. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3609-0_16 · Cited by 16
Self-citationToward a Computational Ecotoxicity Assay
Kamerlin, N., Delcey, M. G., Manzetti, S., & van der Spoel, D. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00574 · Cited by 17
Sellami, A., Montès, M., & Lagarde, N. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22062846 · Cited by 17
Safety assessment of natural products in Malaysia: current practices, challenges, and new strategies
Pauzi, N. A. M., Cheema, M. S., Ismail, A., Ghazali, A. R., & Abdullah, R. (2021) · Reviews on Environmental Health · doi:10.1515/reveh-2021-0072 · Cited by 18
Sosnin, S. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00888-z · Cited by 15
“The overall performance and individual probabilities were estimated using the original test set provided by the author [28].”
MethodChushak, Y. G., Shows, H. W., Gearhart, J. M., & Pangburn, H. A. (2018) · Toxicology Research · doi:10.1039/c7tx00268h · Cited by 16
Machine learning models for predicting endocrine disruption potential of environmental chemicals
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“21 Additionally, we explored the use of Extended-Connectivity Fingerprints 27 (ECF), a class of circular topological finger-prints, as an alternative set of descriptors.”
BackgroundMethodInfluentialSellami, A., Réau, M. F., Montès, M., & Lagarde, N. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.986016 · Cited by 14
“The reinforcement of collaborations between research teams following the example of COMPARA (20) and CERAPP (36) consortiums or the Tox21 challenge (174) can improve the reproducibility since it creates accountability in exchange of a frontiersin.org benchmarked data (172).”
BackgroundMethodInfluentialBouchouireb, Z., Olivier‐Jimenez, D., Jaunet‐Lahary, T., Thany, S. H., & Questel, J. L. (2024) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2024.116582 · Cited by 16
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“The high-throughput bioactivity results from these research programs have been used for chemical screening efforts of regulatory importance, like the Endocrine Disruptor Screening Program (EDSP) [19].”
Xi, Y., Yang, X., Zhang, H., Liu, H., Watson, P. L., & Yang, F. (2019) · Chemosphere · doi:10.1016/j.chemosphere.2019.125135 · Cited by 14
Integrating endocrine-related health effects into comparative human toxicity characterization
Emara, Y., Fantke, P., Judson, R., Chang, X., Pradeep, P., Lehmann, A., Siegert, M., & Finkbeiner, M. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.143874 · Cited by 14
Self-citationPredictive QSAR Modeling: Methods and Applications in Drug Discovery and Chemical Risk Assessment
Golbraikh, A., Wang, X. S., Zhu, H., & Tropsha, A. (2016) · Springer eBooks · doi:10.1007/978-94-007-6169-8_37-3 · Cited by 15
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Self-citationWang, H., Feng, X., Su, W., Zhong, L., Liu, Y., Liang, Y., Ruan, T., & Jiang, G. (2024) · Environmental Science & Technology · doi:10.1021/acs.est.4c10230 · Cited by 13
Bisphenol S modulates concentrations of bisphenol A and oestradiol in female and male mice
Pollock, T., Greville, L. J. S., Weaver, R. E., Radenovic, M., & deCatanzaro, D. (2018) · Xenobiotica · doi:10.1080/00498254.2018.1480818 · Cited by 13
“BPS can bind to ER (Mansouri et al., 2016; Rochester & Bolden, 2015; Rosenmai et al., 2014; Yamasaki et al., 2004), and can also produce an oestrogenic response in a rat uterotrophic assay (Yamasaki et al.”
BackgroundChemometrics for QSAR Modeling
Todeschini, R., Consonni, V., Ballabio, D., & Grisoni, F. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-409547-2.14703-1 · Cited by 14
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Metadata
- 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
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › 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 › Endocrinology › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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