Cited by 377Computational Drug Discovery MethodsEffects and risks of endocrine disrupting chemicalsEstrogen and related hormone effects

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CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

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Mansouri, Kamel, Abdelaziz, Ahmed, Rybacka, Aleksandra, Roncaglioni, Alessandra, Tropsha, Alexander, Varnek, Alexandre, Zakharov, Alexey, Worth, Andrew, Richard, Ann M., Grulke, Christopher M., Trisciuzzi, Daniela, Fourches, Denis, Horvath, Dragos, Benfenati, Emilio, Muratov, Eugene, Wedebye, Eva Bay, Grisoni, Francesca, Mangiatordi, Giuseppe F., Incisivo, Giuseppina M., Hong, Huixiao, Ng, Hui W., Tetko, Igor V., Balabin, Ilya, Kancherla, Jayaram, Shen, Jie, Burton, Julien, Nicklaus, Marc, Cassotti, Matteo, Nikolov, Nikolai G., Nicolotti, Orazio, Andersson, Patrik L., Zang, Qingda, Politi, Regina, Beger, Richard D., Todeschini, Roberto, Huang, Ruili, Farag, Sherif, Rosenberg, Sine A., Slavov, Svetoslav, Hu, Xin, Judson, Richard S.·2016·Environmental Health Perspectives

DOI 10.1289/ehp.1510267PMC PMC4937869

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Abstract

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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Ma, G., Cui, Y., Li, Q., Chen, Y., Guo, P., Feng, D., & Zhao, T. (2025) · Food and Chemical Toxicology · doi:10.1016/j.fct.2025.115410 · Cited by 5
Modeling Tox21 Data for Toxicity Prediction and Mechanism Deconvolution
Xu, T., Xia, M., & Huang, R. (2023) · Computational methods in engineering & the sciences · doi:10.1007/978-3-031-20730-3_19 · Cited by 5
Self-citation
Application of QSAR models based on machine learning methods in chemical risk assessment and drug discovery
Xu, T., Ngan, D. K., & Huang, R. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15339-6.00006-0 · Cited by 5
Self-citation
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Lui, R. (2024) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.4c00050 · Cited by 5
Predictive Models in Ecotoxicology: Bridging the Gap Between Scientific Progress and Regulatory Applicability
Focks, A., Grisoni, F., Barsi, A., & Vighi, M. (2018) · Integrated Environmental Assessment and Management · doi:10.1002/ieam.4039 · Cited by 5
“At themoment, despite the fact that QSARs are generally well perceived for testingprioritizationpurposes (e.g.,Mansouri et al. 2016), they are acceptedas “stand-alone” tools for regulatorypurposes in fewcases (ECHA2008), suchas forpredictingphysicochemical properties, relatively simple fate and…”
BackgroundSelf-citation
Combined Naïve Bayesian, Chemical Fingerprints and Molecular Docking Classifiers to Model and Predict Androgen Receptor Binding Data for Environmentally- and Health-Sensitive Substances
García‐Sosa, A. T., & Maran, U. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22136695 · Cited by 5
“A similar collaborative project was carried out previously to predict compounds with potential estrogenic activity in the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) [43].”
Background
Prediction of Carbonate Selectivity of PVC-Plasticized Sensor Membranes with Newly Synthesized Ionophores through QSPR Modeling
Vladimirova, N. I., Polukeev, V. A., Ashina, J., Бабаин, В. А., Legin, A. V., & Kirsanov, D. O. (2022) · Chemosensors · doi:10.3390/chemosensors10020043 · Cited by 5
An advancement in developmental and reproductive toxicity (DART) risk assessment: evaluation of a bioactivity and exposure-based NAM toolbox
Mueller, I., Abdelkhaliq, A., Carmichael, P. L., Dent, M. P., Feliksik, M., Flatt, L., Houghton, J., Horcas‐Nieto, J. M., Jamalpoor, A., Kukić, P., et al. (2025) · Frontiers in Toxicology · doi:10.3389/ftox.2025.1602065 · Cited by 6
“These are Derek Nexus with selected endpoints, OECD QSAR Toolbox DART Scheme, VEGA_ Additionally, OPERA models are also predicting agonist and antagonist activity.”
Influential
Higher microbial diversity enhances the extent and rate of organic micropollutant biodegradation under aerobic and nitrate-reducing conditions
Ore, A., Palm, E. H., van Wezel, A. P., & Sutton, N. B. (2025) · Water Research · doi:10.1016/j.watres.2025.124921 · Cited by 4
Community‐Engaged Research and the Use of Open Access ToxVal / ToxRef In Vivo Databases and New Approach Methodologies ( NAM ) to Address Human Health Risks From Environmental Contaminants
Silva, M. H., Capps, S., & London, J. K. (2024) · Birth Defects Research · doi:10.1002/bdr2.2395 · Cited by 4
CONTRAST prioritisation tool: filtering and ranking contaminants of emerging concern in the marine environment using hazard-based approaches
Yulikayani, P. Y., Witte, B. D., Ali, A., Barber, J. L., Bellas, J., Briant, N., Brooks, S., Bruvold, A., French, M., Hylland, K., et al. (2025) · Environmental Sciences Europe · doi:10.1186/s12302-025-01257-9 · Cited by 4
“…disrupting potential was assessed using oestrogen receptor activity and androgen receptor activity, predicted using models developed by the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) and the Collaborative Modelling Project for Androgen Receptor Activity (CoMPARA) [42, 43].”
Toxicity profiling and prioritization of plant-derived antimalarial agents
Devillers, J., & Devillers, H. (2019) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2019.1665844 · Cited by 4
“The accuracy, specificity, and sensitivity of the CERAPP model were equal to 0.96, 0.85, and 0.97 for the training set (n = 1288) and 0.74, 0.70, and 0.77 for the test set (Japanese METI data, n = 497).”
MethodInfluential
Toxicity prediction using locality-sensitive deep learner
Yap, X. H., & Raymer, M. L. (2021) · Computational Toxicology · doi:10.1016/j.comtox.2021.100210 · Cited by 4
Mold2 Descriptors Facilitate Development of Machine Learning and Deep Learning Models for Predicting Toxicity of Chemicals
Hong, H., Liu, J., Ge, W., Sakkiah, S., Guo, W., Yavaş, G., Zhang, C., Gong, P., Tong, W., & Patterson, T. A. (2023) · Computational methods in engineering & the sciences · doi:10.1007/978-3-031-20730-3_12 · Cited by 3
Self-citation
Predictive Models for Compound Binding to Androgen and Estrogen Receptors Based on Counter-Propagation Artificial Neural Networks
Stanojević, M., Dolenc, M. S., & Vračko, M. (2023) · Toxics · doi:10.3390/toxics11060486 · Cited by 4
“Mansouri, K.; Abdelaziz, A.; Rybacka, A.; Roncaglioni, A.; Tropsha, A.; Varnek, A.; Zakharov, A.; Worth, A.; Richard, A.M.; Grulke, C.M.; et al. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project.”
Systematic evaluation of the evidence base on methyl tert-butyl ether supporting a lack of concern for carcinogenic hazard in humans based on animal cancer studies and mechanistic data
Borghoff, S. J., Rivera, B. N., Fitch, S., Buerger, A. N., Choksi, N. Y., Franzen, A., Vincent, M. J., Covington, T., Bus, J. S., Rushton, E. K., et al. (2025) · Current Research in Toxicology · doi:10.1016/j.crtox.2025.100224 · Cited by 4
“This review included ToxCast QSAR models for predicting Estrogen Receptor (ER) and Androgen Receptor (AR) binding and activity using the CERAPP (Mansouri et al., 2016) and COMPARA (Mansouri et al., 2020) consensus models, respectively as reported within the EPA CompTox Chemicals Dashboard for MTBE…”
An in silico protocol for endocrine activity assessment: Integrating predictions, experimental evidence, and expert reviews across estrogen, androgen, thyroid, and steroidogenesis modalities
Johnson, C., Marty, S., Kim, M. T., Crofton, K. M., Roncaglioni, A., Bassan, A., Barton‐Maclaren, T. S., Domingues, A., Frericks, M., Karmaus, A. L., et al. (2025) · Computational Toxicology · doi:10.1016/j.comtox.2025.100364 · Cited by 4
Self-citation
Part I. Systematic development of machine learning models for predicting mechanism-based toxicity from in vitro ToxCast bioassay data
Kim, D., Ahn, S., Jeong, J., & Choi, J. (2025) · Computational Toxicology · doi:10.1016/j.comtox.2025.100371 · Cited by 4
Rigorous 3-dimensional spectral data activity relationship approach modeling strategy for ToxCast estrogen receptor data classification, validation, and feature extraction
Slavov, S. H., & Beger, R. D. (2016) · Environmental Toxicology and Chemistry · doi:10.1002/etc.3578 · Cited by 4
“As 1 of the participating groups in the CERAPP initiative the USEPA provided us with 2 already curated datasets: 1) an initial set of 1677 chemicals, and 2) a blind prediction set of 2008 compounds that was received after the model was developed and reported to the USEPA [7].”
BackgroundMethodResultInfluentialSelf-citation
Ternary classification models for predicting hormonal activities of chemicals via nuclear receptors
Lu, Y., Zhang, Q., Huang, F., Nie, W., Hu, C., Ying, H., Dong, X., & Zhao, M. (2018) · Chemical Physics Letters · doi:10.1016/j.cplett.2018.06.022 · Cited by 4
In Silico Ecotoxicological Modeling of Pesticide Metabolites and Mixtures
Chang, C., Chang, C.-W., Wu, F., Chang, L., & Liu, T.-C. (2020) · Methods in pharmacology and toxicology · doi:10.1007/978-1-0716-0150-1_23 · Cited by 4
Visualization and Analysis of the REACH‐chemical Space with Generative Topographic Mapping
Lunghini, F., Marcou, G., Azam, P., Enrici, M., Miert, E. V., & Varnek, A. A. (2020) · Molecular Informatics · doi:10.1002/minf.202000232 · Cited by 4
Self-citation
Evaluating structure-based activity in a high-throughput assay for steroid biosynthesis
Foster, M. J., Patlewicz, G. Y., Shah, I., Haggard, D. E., Judson, R., & Friedman, K. P. (2022) · Computational Toxicology · doi:10.1016/j.comtox.2022.100245 · Cited by 4
Self-citation
Chemistry domain of applicability evaluation against existing estrogen receptor high-throughput assay-based activity models
Nelms, M., Antonijevic, T., Ring, C., Harris, D. L., Bever, R. J., Lynn, S. G., Williams, D. N., Chappell, G. A., Boyles, R., Borghoff, S. J., et al. (2024) · Frontiers in Toxicology · doi:10.3389/ftox.2024.1346767 · Cited by 4
“…whether the inactivity observed in the NVS assay for the chemicals with a true-positive prediction is “ real, ” several have an “ active (weak) ” or “ active (very weak) ” binding prediction from the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) model (Mansouri et al., 2016).”
Method
An Approach for Using In Vitro and In Silico Data to Identify Pharmaceuticals with Potential (Anti-)Estrogenic Activity in Aquatic Vertebrates at Environmentally Relevant Concentrations
Pinto, C. L., Bloom, R. A., & Laurenson, J. P. (2019) · Environmental Toxicology and Chemistry · doi:10.1002/etc.4533 · Cited by 4
“The output of the continuous models is a numeric value (QSAR score) ranging from 0 to 1 (equivalent to the AUC score) that corresponds to the following potency categories (Mansouri et al. 2016): Strong: QSAR score> 0.75; corresponding to an AC50 ≤ 0.09 µM; Moderate: 0.5”
Influential
Computational Tools for ADMET Profiling
Fourches, D., Williams, A. J., Patlewicz, G. Y., Shah, I., Grulke, C., Wambaugh, J. F., Richard, A., & Tropsha, A. (2018) · Computational Toxicology · doi:10.1002/9781119282594.ch8 · Cited by 4
Self-citation
FT-GNN Tool for Bridging HRMS Features and Bioactivity: Uncovering Unidentified Estrogen Receptor Agonists in Sewage
Fan, F., Liu, F., Yu, Q., Yi, R., Ren, H., & Geng, J. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c02324 · Cited by 4
New approach methodologies for evaluating developmental and reproductive toxicity potential: Review on state of the art, applicability for cosmetic related ingredients and coverage of developmental stages
Burbank, M., Hewitt, N. J., Gautier, F., Noel-Voisin, A., Golbamaki, N., Grall, R., Léonard, M., Carron, L., Riu, A., Emery, S., et al. (2025) · NAM journal. · doi:10.1016/j.namjnl.2025.100055 · Cited by 5
“The US Environmental Protection Agency (EPA) led two multi-stakeholder projects in which multiple in silico QSAR and docking models were trained for ER and AR effects using training sets of ~1700 chemicals (Mansouri et al., 2016; Mansouri et al., 2020).”
In silico prediction of endocrine activity
Jiskoot, D. A., Pennings, J. L. A., Peijnenburg, W. J. G. M., van Westen, G. J. P., Jespers, W., & Wassenaar, P. N. H. (2025) · Trends in Endocrinology and Metabolism · doi:10.1016/j.tem.2025.09.011 · Cited by 3
Integrated in silico and in vitro assessment of estrogen and androgen receptor activity of 10 selected PFAS within an Integrated Approach to Testing and Assessment (IATA)
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Seo, M., Choi, J., Park, J., Yu, W., & Kim, S. (2023) · Chemosphere · doi:10.1016/j.chemosphere.2023.140926 · Cited by 3
AI/ML modeling to enhance the capability of in vitro and in vivo tests in predicting human carcinogenicity
Tevosyan, A., Yeghiazaryan, H., Tadevosyan, G., Apresyan, L., Atoyan, V., Misakyan, A., Navoyan, Z. E., Stopper, H., Babayan, N., & Khondkaryan, L. (2025) · Mutation Research/Genetic Toxicology and Environmental Mutagenesis · doi:10.1016/j.mrgentox.2025.503858 · Cited by 3
PikMe: a flexible prioritization tool for chemicals of emerging concern
Wennberg, A. C., Rostkowski, P., & Reid, M. J. (2025) · Environmental Toxicology and Chemistry · doi:10.1093/etojnl/vgaf237 · Cited by 3
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Self-citation
Orthogonal assay for validation of Tox21 PPARγ data and applicability to in silico prediction model
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Estrogenicity of Major Organic Chemicals in Cigarette Sidestream Smoke Particulate Matter
Lin, C., & Li, L. (2023) · Atmosphere · doi:10.3390/atmos14040647 · Cited by 3
Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers
Haddad, S., Oktay, L., Erol, İ., Şahin, K., & Durdağı, S. (2023) · ACS Omega · doi:10.1021/acsomega.3c06074 · Cited by 3
Application of Evolving New Approach Methodologies for Chemical Safety Assessment
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QSAR: Using the Past to Study the Present
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Evaluation of the endocrine disrupting potential of Di-isodecyl phthalate
Lea, I. A., Feifarek, D. J., Mihalchik, A., Heintz, M. M., Haws, L. C., Nyambego, H., Goyak, K. O., Palermo, C. M., & Borghoff, S. J. (2025) · Current Research in Toxicology · doi:10.1016/j.crtox.2025.100221 · Cited by 3
The TTC Data Mart: An interactive browser for threshold of toxicological concern calculations
Nicolas, C. I., Bronson, K., Pendse, S. N., Efremenko, A. Y., Fitzpatrick, J. M., Minto, M. S., Mansouri, K., Yoon, M., Phillips, M. B., Clewell, R. A., et al. (2020) · Computational Toxicology · doi:10.1016/j.comtox.2020.100128 · Cited by 3
Self-citation
An in silico to in vivo approach identifies retinoid-X receptor activating tert-butylphenols used in food contact materials
Mengeling, B. J., Ramaprasad, A. S. E., Smith, M. T., Turkieh, D., Kleinstreuer, N., Mansouri, K., Durkin, K. A., Merrill, M. A. L., & Furlow, J. D. (2025) · Scientific Reports · doi:10.1038/s41598-025-09244-z · Cited by 2
“We expanded the list of screened chemicals from the CoMPARA projects’ original list of 55,450 chemicals, which built on CERAPP’s original list of 32,464 chemicals 56 compiled from a library of over 50,000 chemicals that humans are potentially exposed to.”
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
DOI: 10.1289/ehp.1510267
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

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