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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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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · cited 1×
“Some of the participants applied feature selection techniques, such as genetic algorithms (GAs) (Davi 1991) and random forest (RF) (Breiman 2001).”
Nearest neighbor pattern classification
Cover, T., & Hart, P. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1053964 · Cited by 16,936 · cited 2×
“…al. 2001; Beger and Wilkes 2001; Slavov et al. 2013), support vector machines (SVM) (Cristianini and Shawe-Taylor 2000), k nearest neighbors (kNN) (Cover and Hart 1967; Kowalski and Bender 1972), associative artificial neural networks (ASNN) (Tetko 2002a, 2002b), PASS algorithm derived from Naïve…”
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855 · cited 3×
“…spectral data–activity relationship (QSDAR) (Beger et al. 2001; Beger and Wilkes 2001; Slavov et al. 2013), support vector machines (SVM) (Cristianini and Shawe-Taylor 2000), k nearest neighbors (kNN) (Cover and Hart 1967; Kowalski and Bender 1972), associative artificial neural…”
PLS-regression: a basic tool of chemometrics
Wold, S., Sjöström, M., & Eriksson, L. (2001) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(01)00155-1 · Cited by 10,084
Handbook of Genetic Algorithms
Davis, L. D. (1991) · Cumulative Index of Computer Aided Architectural Design · Cited by 7,330
Endocrine-Disrupting Chemicals: An Endocrine Society Scientific Statement
Diamanti-Kandarakis, E., Bourguignon, J.-P., Giudice, L. C., Hauser, R., Prins, G. S., Soto, A. M., Zoeller, R. T., & Gore, A. C. (2009) · Endocrine Reviews · doi:10.1210/er.2009-0002 · Cited by 4,585 · cited 4×
“Exposure to EDCs can lead to adverse health effects involving developmental, neurological, reproductive, metabolic, cardiovascular, and immune systems in humans and wildlife (Colborn et al. 1993; Davis et al. 1993; Diamanti-Kandarakis et al. 2009).”
BackgroundInfluentialKuiper, G. G. J. M., Carlsson, B., Grandien, K., Enmark, E., Häggblad, J., Nilsson, S., & Gustafsson, J.-A. (1997) · Endocrinology · doi:10.1210/endo.138.3.4979 · Cited by 4,442 · cited 1×
“2015) ASNN DRAGON METI + (Kuiper et al. 1997; Taha et al. 2010) Categorical UNC_MML SVM+RF DRAGON ToxCastTM (120) Categorical UNIBA (Trisciuzzi et al.”
ChEMBL: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr777 · Cited by 4,382 · cited 2×
“(Gaulton et al. 2012).”
Developmental Effects of Endocrine-Disrupting Chemicals in Wildlife and Humans
Colborn, T., Saal, F. S. V., & Soto, A. M. (1993) · Environmental Health Perspectives · doi:10.1289/ehp.93101378 · Cited by 3,570 · cited 1×
“Exposure to EDCs can lead to adverse health effects involving developmental, neurological, reproductive, metabolic, cardiovascular, and immune systems in humans and wildlife (Colborn et al. 1993; Davis et al. 1993; Diamanti-Kandarakis et al. 2009).”
PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints
Yap, C. W. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21707 · Cited by 2,979
Research works printed but not linked: 3
- Hong H, Slavov S, Ge W, Qian F, Su Z, Fang H, et al, 2012, Mold2 molecular descriptors for QSAR., 65
- NIH (National Institutes of Health) . 2015. The PubChem Database.Available: http://pubchem.ncbi.nlm.nih.gov/ [accessed 26 January 2015].
- U.S. EPA, 2014, CPCat: Chemical and Product Categories. Exploring consumer exposure pathways and patterns of use for chemicals in the environment., Toxicology Reports, 2, 228
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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
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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 400
“…of chemical alternatives (Chen et al. , 2016; NRC, 2014); prioritizing chemicals for biomonitoring (Krowech et al. , 2016); and building training sets for quantitative structure-activity relationship (QSAR) modeling (Mansouri et al. , 2016a; Norinder and Boyer, 2016; Sedykh et al. , 2011).”
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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 220
“For example, the Collaborative Estrogen Receptor Activity Prediction Project recently predicted ER activity across a set over 32,000 chemical structures [73].”
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“This work fl ow was designed so that chemicals with the same standardized QSAR-ready structures would automatically have the same computational predictions (Fourches et al. 2010, 2016; Mansouri et al. 2016a).”
InfluentialSelf-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
“6 As only a fraction of these chemicals have been fully assessed and characterized, 7–9 there is a need for more rapid and inexpensive approaches to prioritize thousands of chemicals for mechanistically relevant toxicity testing.”
BackgroundSelf-citationCritical Assessment of Small Molecule Identification 2016: automated methods
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“The combination of the Category 2 submissions resulted in even better overall performance than each individual method, indicating the complementarity of the approaches and supporting the potential use of consensus fragmentation results as has been shown earlier for fragmenters [12, 52] and also recently for toxicity m…”
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 202
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 182
“The chemical structures were curated and standardized for modeling using a published, freely available work-flow [13, 30].”
MethodSelf-citationComparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction
Russo, D. P., Zorn, K. M., Clark, A. M., Zhu, H., & Ekins, S. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.8b00546 · Cited by 152
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 152
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 155
Self-citationEvaluation of estrogen receptor alpha activation by glyphosate-based herbicide constituents
Mesnage, R., Phedonos, A., Biserni, M., Arno, M. J., Balu, S., Corton, J. C., Ugarte, R., & Antoniou, M. N. (2017) · Food and Chemical Toxicology · doi:10.1016/j.fct.2017.07.025 · Cited by 153
“Adapter sequences (standard TruSeq LT adapter seq) were removed/trimmed using cutadapt (Martin, 2011).”
MethodWang, Z., Chen, J., & Hong, H. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.0c07040 · Cited by 138
Self-citationMansouri, K., Grulke, C., Richard, A. M., Judson, R., & Williams, A. J. (2016) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2016.1253611 · Cited by 137
“After the curation of the complete list of PHYSPROP datasets, the selected chemical structures with 3 and 4 STAR quality levels were run through the 2D QSAR-ready standardization workflow to prepare them for molecular descriptor calculation and QSAR modelling [10].”
BackgroundMethodInfluentialSelf-citationMore, S. J., Bampidis, V., Benford, D., Bragard, C., Hernández, A. F., Bennekou, S. H., Halldorsson, T. I., Koutsoumanis, K. P., Lambré, C., Machera, K. A., et al. (2021) · EFSA Journal · doi:10.2903/j.efsa.2021.7033 · Cited by 118
Self-citationNavigating through the minefield of read-across tools: A review of in silico tools for grouping
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“Some efforts have been made to address the issue of interpretation and combination of predictions from multiple QSAR tools [30,44,24].”
BackgroundMcCord, J. P., Groff, L. C., & Sobus, J. R. (2021) · Environment International · doi:10.1016/j.envint.2021.107011 · Cited by 118
Identifying known unknowns using the US EPA’s CompTox Chemistry Dashboard
McEachran, A. D., Sobus, J. R., & Williams, A. J. (2016) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-016-0139-z · Cited by 108
Zhong, S., Hu, J., Fan, X., Yu, X. B., & Zhang, H. J. (2019) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2019.121141 · Cited by 111
Structure-Based Prediction of hERG-Related Cardiotoxicity: A Benchmark Study
Creanza, T. M., Delre, P., Ancona, N., Lentini, G., Saviano, M., & Mangiatordi, G. F. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00744 · Cited by 114
Self-citationLigand efficiency metrics in drug discovery: the pros and cons from a practical perspective
Cavalluzzi, M. M., Mangiatordi, G. F., Nicolotti, O., & Lentini, G. (2017) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2017.1365056 · Cited by 106
Self-citationSirenko, O., Grimm, F. A., Ryan, K., Iwata, Y., Chiu, W. A., Parham, F., Wignall, J., Anson, B. D., Cromwell, E. F., Behl, M., et al. (2017) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2017.02.020 · Cited by 107
Benfenati, E., Chaudhry, Q., Gini, G. C., & Dorne, J. L. C. M. (2019) · Environment International · doi:10.1016/j.envint.2019.105060 · Cited by 113
“For example, Mansouri et al. integrated with numerous approaches the results of 48 models to predict estrogen receptor activity (Mansouri et al., 2016) and observed that no single method outperformed the others.”
ResultSelf-citationCote, I., Andersen, M. E., Ankley, G. T., Barone, S., Birnbaum, L. S., Boekelheide, K., Bois, F. Y., Burgoon, L. D., Chiu, W. A., Crawford‐Brown, D., et al. (2016) · Environmental Health Perspectives · doi:10.1289/ehp233 · Cited by 101
Self-citationHong, H., Thakkar, S., Chen, M., & Tong, W. (2017) · Scientific Reports · doi:10.1038/s41598-017-17701-7 · Cited by 99
Self-citationPhillips, K. A., Wambaugh, J. F., Grulke, C., Dionisio, K. L., & Isaacs, K. (2016) · Green Chemistry · doi:10.1039/c6gc02744j · Cited by 101
Self-citationReview of machine learning and deep learning models for toxicity prediction
Guo, W., Liu, J., Dong, F., Song, M., Li, Z., Khan, M. K. H., Patterson, T. A., & Hong, H. (2023) · Experimental Biology and Medicine · doi:10.1177/15353702231209421 · Cited by 111
Self-citationKleinstreuer, N., Karmaus, A. L., Mansouri, K., Allen, D., Fitzpatrick, J. M., & Patlewicz, G. Y. (2018) · Computational Toxicology · doi:10.1016/j.comtox.2018.08.002 · Cited by 92
Self-citationRosenmai, A. K., Winge, S. B., Möller, M., Lundqvist, J., Wedebye, E. B., Nikolov, N. G., Johansson, H. K. L., & Vinggaard, A. M. (2020) · Chemosphere · doi:10.1016/j.chemosphere.2020.127703 · Cited by 92
Self-citationGadaleta, D., Lombardo, A., Toma, C., & Benfenati, E. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0315-6 · Cited by 92
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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“Workflows describing the generation of QSAR-Ready structures have previously been published [27, 28, 30].”
MethodInfluentialSelf-citationMolecular dynamics simulations and applications in computational toxicology and nanotoxicology
Selvaraj, C., Sakkiah, S., Tong, W., & Hong, H. (2017) · Food and Chemical Toxicology · doi:10.1016/j.fct.2017.08.028 · Cited by 87
Self-citationTan, H., Wang, X., Hong, H., Benfenati, E., Giesy, J. P., Gini, G. C., Kusko, R., Zhang, X., Yu, H., & Shi, W. F. (2020) · Environmental Science & Technology · doi:10.1021/acs.est.0c02639 · Cited by 89
Self-citationDing, Y., Chen, M., Guo, C., Zhang, P., & Wang, J. (2020) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2020.115212 · Cited by 88
Chiu, W. A., Axelrad, D. A., Dalaijamts, C., Dockins, C., Shao, K., Shapiro, A. J., & Paoli, G. M. (2018) · Environmental Health Perspectives · doi:10.1289/ehp3368 · Cited by 81
Wignall, J., Muratov, E. N., Sedykh, A., Guyton, K. Z., Tropsha, A., Rusyn, I., & Chiu, W. A. (2018) · Environmental Health Perspectives · doi:10.1289/ehp2998 · Cited by 82
“For comparison, CDK descriptors were also generated for the Environmental Health Perspectives 057008-3 126(5) May 2018 32,464 structures in the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) “prediction” data set (Mansouri et al. 2016).”
InfluentialSelf-citationWilliams, A. J., Lambert, J. C., Thayer, K. A., & Dorne, J. L. C. M. (2021) · Environment International · doi:10.1016/j.envint.2021.106566 · Cited by 78
“CERAPP: Collaborative Estrogen Receptor Activity Prediction Project.”
BackgroundMethodInfluentialGadaleta, D., Manganelli, S., Roncaglioni, A., Toma, C., Benfenati, E., & Mombelli, E. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00297 · Cited by 77
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Grisoni, F., Consonni, V., & Todeschini, R. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8639-2_5 · Cited by 77
Self-citationZakharov, A. V., Zhao, T., Nguyễn, Ð.-T., Peryea, T., Sheils, T. K., Yasgar, A., Huang, R., Southall, N. T., & Simeonov, A. M. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00526 · Cited by 80
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“EPA’s Estimation Program Interface [342].”
BackgroundMethodInfluentialEvaluation of food-relevant chemicals in the ToxCast high-throughput screening program
Karmaus, A. L., Filer, D. L., Martin, M. T., & Houck, K. A. (2016) · Food and Chemical Toxicology · doi:10.1016/j.fct.2016.04.012 · Cited by 71
Alves, V. M., Auerbach, S. S., Kleinstreuer, N., Rooney, J. P., Muratov, E. N., Rusyn, I., Tropsha, A., & Schmitt, C. (2021) · Alternatives to Laboratory Animals · doi:10.1177/02611929211029635 · Cited by 71
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Bell, S. M., Abedini, J., Ceger, P. C., Chang, X., Cook, B. A., Karmaus, A. L., Lea, I. A., Mansouri, K., Phillips, J., McAfee, E., et al. (2020) · Toxicology in Vitro · doi:10.1016/j.tiv.2020.104916 · Cited by 69
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Chibwe, L., Titaley, I. A., Hoh, E., & Simonich, S. L. M. (2017) · Environmental Science & Technology Letters · doi:10.1021/acs.estlett.6b00455 · Cited by 70
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Liu, J., Patlewicz, G. Y., Williams, A. J., Thomas, R. S., & Shah, I. (2017) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.7b00084 · Cited by 72
Prediction of Hydrolysis Products of Organic Chemicals under Environmental pH Conditions
Tebes-Stevens, C. L., Patel, J. M., Jones, W. J., & Weber, E. J. (2017) · Environmental Science & Technology · doi:10.1021/acs.est.6b05412 · Cited by 67
Mansouri, K., Moreira‐Filho, J. T., Lowe, C. N., Charest, N., Martin, T. M., Tkachenko, V., Judson, R., Conway, M., Kleinstreuer, N., & Williams, A. J. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00814-3 · Cited by 72
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In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9
Nembri, S., Grisoni, F., Consonni, V., & Todeschini, R. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17060914 · Cited by 66
Self-citationCiallella, H. L., Russo, D. P., Aleksunes, L. M., Grimm, F. A., & Zhu, H. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.1c02656 · Cited by 63
In Silico Predictions of Endocrine Disruptors Properties
Schneider, M., Pons, J.-L., Labesse, G., & Bourguet, W. (2019) · Endocrinology · doi:10.1210/en.2019-00382 · Cited by 69
Tan, H., Chen, Q., Hong, H., Benfenati, E., Gini, G. C., Zhang, X., Yu, H., & Shi, W. F. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.1c04997 · Cited by 59
Self-citationCiallella, H. L., Russo, D. P., Aleksunes, L. M., Grimm, F. A., & Zhu, H. (2020) · Laboratory Investigation · doi:10.1038/s41374-020-00477-2 · Cited by 62
Application of new approach methodologies: ICE tools to support chemical evaluations
Abedini, J., Cook, B. A., Bell, S. M., Chang, X., Choksi, N. Y., Daniel, A. B., Hines, D. E., Karmaus, A. L., Mansouri, K., McAfee, E., et al. (2021) · Computational Toxicology · doi:10.1016/j.comtox.2021.100184 · Cited by 56
Self-citationBurnett, S. D., Blanchette, A. D., Chiu, W. A., & Rusyn, I. (2021) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.1c00203 · Cited by 59
Developing context appropriate toxicity testing approaches using new alternative methods (NAMs)
Andersen, M. E. (2019) · ALTEX · doi:10.14573/altex.1906261 · Cited by 60
“diction project (CERAPP), large-scale modeling using 32,464 structures showed the possibility of screening large libraries of chemicals using a consensus of different in silico approaches (Mansouri et al., 2016).”
BackgroundMethodOECD approaches and considerations for regulatory evaluation of endocrine disruptors
Browne, P., van der Wal, L., & Gourmelon, A. (2019) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2019.110675 · Cited by 58
Lee, S., & Barron, M. G. (2017) · PLoS ONE · doi:10.1371/journal.pone.0169607 · Cited by 53
Alves, V. M., Golbraikh, A., Capuzzi, S. J., Liu, K., Lam, W. I., Korn, D., Pozefsky, D., Andrade, C. H., Muratov, E. N., & Tropsha, A. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00124 · Cited by 56
Self-citationConsensus versus Individual QSARs in Classification: Comparison on a Large-Scale Case Study
Valsecchi, C., Grisoni, F., Consonni, V., & Ballabio, D. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b01057 · Cited by 52
Self-citationGrisoni, F., Consonni, V., & Ballabio, D. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00794 · Cited by 52
Self-citationMachine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction
Zorn, K. M., Foil, D. H., Lane, T. R., Russo, D. P., Hillwalker, W., Feifarek, D. J., Jones, F. E., Klaren, W. D., Brinkman, A. M., & Ekins, S. (2020) · Environmental Science & Technology · doi:10.1021/acs.est.0c03982 · Cited by 54
Chemical Risk Assessment: Traditional vs Public Health Perspectives
Gwinn, M. R., Axelrad, D. A., Bahadori, T., Bussard, D., Cascio, W. E., Deener, K., Dix, D. J., Thomas, R. S., Kavlock, R. J., & Burke, T. A. (2017) · American Journal of Public Health · doi:10.2105/ajph.2017.303771 · Cited by 54
“Advances in the development of chemical libraries, cheminformatics, and read-across predictions and integration with molecular data and adverse outcome pathways have significantly improved their application and predictive capacity, which will allow more comprehensive assessment of the health effects of exposures.(17,1…”
BackgroundChemical representation learning for toxicity prediction
Born, J., Markert, G., Janakarajan, N., Kimber, T. B., Volkamer, A., Martínez, M. R., & Manica, M. (2023) · Digital Discovery · doi:10.1039/d2dd00099g · Cited by 55
Protecting Human and Animal Health: The Road from Animal Models to New Approach Methods
Kaplan, B. L. F., Hoberman, A. M., Slikker, W., Smith, M. A., Corsini, E., Knudsen, T. B., Marty, S., Sobrian, S. K., Fitzpatrick, S. C., Ratner, M. H., et al. (2024) · Pharmacological Reviews · doi:10.1124/pharmrev.123.000967 · Cited by 51
Twenty years of transcriptomics, 17alpha-ethinylestradiol, and fish
Martyniuk, C. J., Feswick, A., Munkittrick, K. R., Dreier, D. A., & Denslow, N. D. (2019) · General and Comparative Endocrinology · doi:10.1016/j.ygcen.2019.113325 · Cited by 48
“Likewise, since these assays screen large chemical libraries with high structural diversity, this information is also useful to construct robust quantitative structure-activity relationship (QSAR) models that predict estrogen receptor binding and activity [72].”
BackgroundDelre, P., Lavado, G. J., Lamanna, G., Saviano, M., Roncaglioni, A., Benfenati, E., Mangiatordi, G. F., & Gadaleta, D. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.951083 · Cited by 51
“, 2021) as testified by recent literature (Mansouri et al., 2016; Kamel et al., 2017).”
BackgroundSelf-citationUncertainty quantification in ToxCast high throughput screening
Watt, E. D., & Judson, R. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196963 · Cited by 46
Self-citationFranzosa, J. A., Bonzo, J. A., Jack, J., Baker, N., Kothiya, P., Witek, R. P., Hurban, P., Siferd, S., Hester, S., Shah, I., et al. (2021) · npj Systems Biology and Applications · doi:10.1038/s41540-020-00166-2 · Cited by 46
A comprehensive review of regulatory test methods for endocrine adverse health effects
Manibusan, M. K., & Touart, L. W. (2017) · Critical Reviews in Toxicology · doi:10.1080/10408444.2016.1272095 · Cited by 45
“These new methods would be used to screen and prioritize chemicals for their potential to interact with the endocrine system (Reif et al. 2010; Rotroff et al. 2014; Browne et al. 2015; Judson et al. 2015; Mansouri et al. 2016).”
MethodWang, L., Zhao, L., Liu, X., Fu, J., & Zhang, A. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.1c01228 · Cited by 47
Tetko, I. V., Maran, U., & Tropsha, A. (2016) · Molecular Informatics · doi:10.1002/minf.201600082 · Cited by 44
Self-citationCiallella, H. L., Russo, D. P., Sharma, S., Li, Y., Sloter, E. D., Sweet, L., Huang, H., & Zhu, H. (2022) · Environmental Science & Technology · doi:10.1021/acs.est.2c01040 · Cited by 45
Margina, D. M., Nițulescu, G. M., Ungurianu, A., Mesnage, R., Γούμενου, Μ., Sarigiannis, D. A., Aschner, M., Spandidos, D. A., Renieri, E. A., Hernández, A. F., et al. (2019) · World Academy of Sciences Journal · doi:10.3892/wasj.2019.17 · Cited by 42
“In the case of estrogen receptors, a large range of methods has been applied in large-scale modeling projects (49).”
MethodThe predictivity of QSARs for toxicity: Recommendations for improving model performance
Cronin, M., Basiri, H., Chrysochoou, G., Enoch, S. J., Firman, J. W., Sp̂înu, N., & Madden, J. C. (2024) · Computational Toxicology · doi:10.1016/j.comtox.2024.100338 · Cited by 41
Cronin, M., & Richarz, A.-N. (2017) · Applied In Vitro Toxicology · doi:10.1089/aivt.2017.0021 · Cited by 41
“84 Extending the endocrine disruption paradigm, the work of Wu et al. 59 mapped and compiled known mechanisms of developmental toxicity, supporting them with data and structural alerts.”
Crofton, K. M., Bassan, A., Behl, M., Chushak, Y. G., Fritsche, E., Gearhart, J. M., Marty, M. S., Mumtaz, M. M., Pavan, M., Ruíz, P., et al. (2022) · Computational Toxicology · doi:10.1016/j.comtox.2022.100223 · Cited by 45
Ruiz, P., Sack, A., Wampole, M., Bobst, S., & Vračko, M. (2017) · Chemosphere · doi:10.1016/j.chemosphere.2017.03.026 · Cited by 40
“Results and discussion 339 The performance and predictive ability of in silico methods, such as QSAR and docking, were 340 investigated and compared with the U.S. EPA EDSP Tier 1 androgen assay results and with the 341 CERAPP training set estrogen results.”
BackgroundMethodResultInfluentialTan, H., Wu, J., Zhang, R., Zhang, C., Li, W., Chen, Q., Zhang, X., Yu, H., & Shi, W. F. (2022) · Environmental Science & Technology · doi:10.1021/acs.est.2c02242 · Cited by 39
Tachachartvanich, P., Sangsuwan, R., Ruiz, H. S., Sanchez, S. S., Durkin, K. A., Zhang, L., & Smith, M. T. (2018) · Environmental Science & Technology · doi:10.1021/acs.est.7b04832 · Cited by 39
Systematic Green Solvent Selection for the Hydroformylation of Long-Chain Alkenes
Linke, S., McBride, K., & Sundmacher, K. (2020) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.0c02611 · Cited by 39
Kay, J. E., Brody, J. G., Schwarzman, M. R., & Rudel, R. A. (2024) · Environmental Health Perspectives · doi:10.1289/ehp13233 · Cited by 39
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
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
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 39
“The chemicals were identi fi ed using results from the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) (Mansouri et al. 2016) and the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) (Mansouri et al. 2020).”
InfluentialMitchell, C. A., Burden, N., Bonnell, M. A., Hecker, M., Hutchinson, T. H., Jagla, M. M., LaLone, C. A., Lagadic, L., Lynn, S. G., Shore, B., et al. (2023) · Environmental Toxicology and Chemistry · doi:10.1002/etc.5584 · Cited by 36
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
Self-citationMolecular Docking for Predictive Toxicology
Trisciuzzi, D., Alberga, D., Leonetti, F., Novellino, E., Nicolotti, O., & Mangiatordi, G. F. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7899-1_8 · Cited by 36
Self-citation
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
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