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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.”
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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
A Statistical View of Some Chemometrics Regression Tools
Frank, L. E., & Friedman, J. H. (1993) · Technometrics · doi:10.1080/00401706.1993.10485033 · Cited by 2,217 · cited 2×
“Models were developed using both well-known and innovative methods including partial least-squares (PLS) (Ståhle and Wold 1987; Wold et al. 2001), partial least-squares discriminant analysis (PLS-DA) (Frank and Friedman 1993; Nouwen et al. 1997), decision forest (DF) (Hong et al. 2005, 2004; Tong et al. 2003; Xie et a…”
State-of-the-science of endocrine disrupting chemicals, 2012
Bergman, Å., Heindel, J., Jobling, S., Kidd, K., & Zoeller, R. T. (2012) · Toxicology Letters · doi:10.1016/j.toxlet.2012.03.020 · Cited by 1,388
A New Substituent Constant, π, Derived from Partition Coefficients
Fujita, T., Iwasa, J., & Hansch, C. H. (1964) · Journal of the American Chemical Society · doi:10.1021/ja01077a028 · Cited by 1,371 · cited 1×
“QSARs have been promoted and their use recognized since the pioneering work of Hansch in the 1960s (Fujita et al. 1964; Hansch et al. 1962; Hansch and Deutsch 1966).”
Kavlock, R. J., Daston, G. P., DeRosa, C., Fenner-Crisp, P. A., Gray, L. E., Kaattari, S. L., Lucier, G. W., Luster, M. I., Mac, M. J., Maczka, C., et al. (1996) · Environmental Health Perspectives · doi:10.1289/ehp.96104s4715 · Cited by 1,235 · cited 1×
“This concern about estrogen-like activity of man-made chemicals is because of their potential for negatively affecting reproductive function (Hileman 1994; Kavlock et al. 1996).”
HANSCH, C., MALONEY, P. P., FUJITA, T., & MUIR, R. M. (1962) · Nature · doi:10.1038/194178b0 · Cited by 1,056 · cited 1×
“QSARs have been promoted and their use recognized since the pioneering work of Hansch in the 1960s (Fujita et al. 1964; Hansch et al. 1962; Hansch and Deutsch 1966).”
The ToxCast Program for Prioritizing Toxicity Testing of Environmental Chemicals
Dix, D. J., Houck, K. A., Martin, M. T., Richard, A. M., Setzer, R. W., & Kavlock, R. J. (2006) · Toxicological Sciences · doi:10.1093/toxsci/kfl103 · Cited by 1,013 · cited 2×
“The Toxicology Testing in the 21st Century (Tox21) collaboration and the U.S. EPA’s Toxicity ForeCaster (ToxCast™) projects are screening thousands of chemicals in HTS in vitro assays for a broad range of targets (Dix et al. 2007; Judson et al. 2010; Martin et al. 2010).”
Self-citationFourches, D., Muratov, E., & Tropsha, A. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100176x · Cited by 832 · cited 1×
“Several different curation approaches were combined into a unique procedure used for this project (Fourches et al. 2010; Wedebye et al. 2013).”
Self-citationSushko, I., Novotarskyi, S., Körner, R., Pandey, A. K., Rupp, M., Teetz, W., Brandmaier, S., Abdelaziz, A., Prokopenko, V. V., Tanchuk, V. Y., et al. (2011) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-011-9440-2 · Cited by 707
Self-citationComparison of Different Approaches to Define the Applicability Domain of QSAR Models
Sahigara, F., Mansouri, K., Ballabio, D., Mauri, A., Consonni, V., & Todeschini, R. (2012) · Molecules · doi:10.3390/molecules17054791 · Cited by 645
Self-citationTransforming Environmental Health Protection
Collins, F. S., Gray, G. M., & Bucher, J. R. (2008) · Science · doi:10.1126/science.1154619 · Cited by 630
Improving the Human Hazard Characterization of Chemicals: A Tox21 Update
Tice, R. R., Austin, C. P., Kavlock, R. J., & Bucher, J. R. (2013) · Environmental Health Perspectives · doi:10.1289/ehp.1205784 · Cited by 619
Judson, R., Houck, K. A., Kavlock, R. J., Knudsen, T. B., Martin, M. T., Mortensen, H. M., Reif, D. M., Rotroff, D. M., Shah, I., Richard, A. M., et al. (2009) · Environmental Health Perspectives · doi:10.1289/ehp.0901392 · Cited by 610 · cited 4×
“The Toxicology Testing in the 21st Century (Tox21) collaboration and the U.S. EPA’s Toxicity ForeCaster (ToxCast™) projects are screening thousands of chemicals in HTS in vitro assays for a broad range of targets (Dix et al. 2007; Judson et al. 2010; Martin et al. 2010).”
InfluentialSelf-citationAlternative (non-animal) methods for cosmetics testing: current status and future prospects—2010
Adler, S., Basketter, D., Creton, S. K., Pelkonen, O., van Benthem, J., Zuang, V., Andersen, K. E., Angers‐Loustau, A., Aptula, A. O., Bal‐Price, A., et al. (2011) · Archives of Toxicology · doi:10.1007/s00204-011-0693-2 · Cited by 596 · cited 1×
“The emergence of concerns about EDCs has resulted in regulations requiring assessment of chemicals for estrogenic activity [Adler et al. 2011; U.S. Environmental Protection Agency (EPA) 1996; U.S. Food and Drug Administration (FDA) 1996].”
Self-citationMedical Hypothesis: Xenoestrogens As Preventable Causes of Breast Cancer
Davis, D. L., Bradlow, H. L., Wolff, M. S., Woodruff, T. J., Hoel, D. G., & Anton‐Culver, H. (1993) · Environmental Health Perspectives · doi:10.1289/ehp.93101372 · Cited by 533 · 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).”
Shanle, E. K., & Xu, W. (2010) · Chemical Research in Toxicology · doi:10.1021/tx100231n · Cited by 521
The Toxicity Data Landscape for Environmental Chemicals
Judson, R., Richard, A., Dix, D. J., Houck, K. A., Martin, M. T., Kavlock, R. J., Dellarco, V. L., Henry, T. R., Holderman, T. S., Sayre, P. G., et al. (2008) · Environmental Health Perspectives · doi:10.1289/ehp.0800168 · Cited by 510 · cited 3×
“Consequently, toxicological data and especially estrogenic activity data are available only for a limited number of compounds (Cohen Hubal et al. 2010; Egeghy et al. 2012; Judson et al. 2009).”
Self-citationPartial least squares analysis with cross‐validation for the two‐class problem: A Monte Carlo study
Ståhle, L., & Wold, S. (1987) · Journal of Chemometrics · doi:10.1002/cem.1180010306 · Cited by 484
How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)
Dearden, J. C., Cronin, M. T. D., & Kaiser, K. L. E. (2009) · SAR and QSAR in Environmental Research · doi:10.1080/10629360902949567 · Cited by 468 · cited 1×
“Several guidance documents to develop these modeling techniques are available in the literature (Dearden et al. 2009; Worth et al. 2005).”
Cancer and developmental exposure to endocrine disruptors.
Birnbaum, L. S., & Fenton, S. E. (2002) · Environmental Health Perspectives · doi:10.1289/ehp.5686 · Cited by 460
Wetmore, B. A., Wambaugh, J. F., Ferguson, S. S., Sochaski, M. A., Rotroff, D. M., Freeman, K., Clewell, H. J., Dix, D. J., Andersen, M. E., Houck, K. A., et al. (2012) · Toxicological Sciences · doi:10.1093/toxsci/kfr254 · Cited by 415
Self-citationMold2, Molecular Descriptors from 2D Structures for Chemoinformatics and Toxicoinformatics
Hong, H., Xie, Q., Ge, W., Qian, F., Fang, H., Shi, L. M., Su, Z., Perkins, R., & Tong, W. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci800038f · Cited by 328 · cited 2×
“…neighborhoods of atoms (MNA, QNA) used by GUSAR and PASS (Filimonov et al. 2009; Poroikov et al. 2000), DRAGON (Talete srl 2012), Mold2 (Hong et al. 2008, 2012), GLIDE (version 6.5, http:// www.schrodinger.com/Glide), AutoDock (Goodsell et al. 1996), ISIDA (Varnek et al. 2008), and…”
InfluentialSelf-citationJudson, R., Magpantay, F. M. G., Chickarmane, V. S., Haskell, C., Tania, N., TAYLOR, J. D., Xia, M., Huang, R., Rotroff, D. M., Filer, D. L., et al. (2015) · Toxicological Sciences · doi:10.1093/toxsci/kfv168 · Cited by 309 · cited 7×
“Concentration-response data from a collection of 18 in vitro HTS assays exploring multiple sites in the mammalian ER pathway were generated for 1,812 chemicals (Judson et al. 2015; U.S. EPA 2014c).”
InfluentialSelf-citationShukla, S. J., Huang, R., Austin, C. P., & Xia, M. (2010) · Drug Discovery Today · doi:10.1016/j.drudis.2010.07.007 · Cited by 291 · cited 1×
“Thus, a series of transformations was applied on the structures to standardize nitro and azide mesomers, keto-enol tautomers, enamine-imine tautomers, ynol-ketene, and other conversions (ChemAxon 2014; Reusch 2013; Sitzmann et al. 2010).”
Self-citationThe Tox21 robotic platform for the assessment of environmental chemicals – from vision to reality
Attene-Ramos, M. S., Miller, N., Huang, R., Michael, S., Itkin, M., Kavlock, R. J., Austin, C. P., Shinn, P., Simeonov, A., Tice, R. R., et al. (2013) · Drug Discovery Today · doi:10.1016/j.drudis.2013.05.015 · Cited by 255 · cited 1×
“EPA’s HTS assays, online databases, and other data sets used by participants to train models: • HTS data from Tox21 project consisting of ~ 8,000 chemicals evaluated in four assays (Attene-Ramos et al. 2013; Collins et al. 2008; Huang et al. 2014; Shukla et al. 2010; Tice et al. 2013), extending beyond the 1,677 used…”
Self-citationPoroikov, V., Filimonov, D. A., Borodina, Y. V., Lagunin, A. A., & Kos, A. J. (2000) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci000383k · Cited by 254
Decision Forest: Combining the Predictions of Multiple Independent Decision Tree Models
Tong, W., Hong, H., Fang, H., Xie, Q., & Perkins, R. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci020058s · Cited by 233 · cited 1×
“…partial least-squares discriminant analysis (PLS-DA) (Frank and Friedman 1993; Nouwen et al. 1997), decision forest (DF) (Hong et al. 2005, 2004; Tong et al. 2003; Xie et al. 2005), three-dimensional (3D) quantitative spectral data–activity relationship (QSDAR) (Beger et al. 2001; Beger and…”
Self-citationMartin, M. T., Dix, D. J., Judson, R., Kavlock, R. J., Reif, D. M., Richard, A. M., Rotroff, D. M., Romanov, S., Medvedev, A. V., Poltoratskaya, N., et al. (2010) · Chemical Research in Toxicology · doi:10.1021/tx900325g · Cited by 217 · cited 2×
“The Toxicology Testing in the 21st Century (Tox21) collaboration and the U.S. EPA’s Toxicity ForeCaster (ToxCast™) projects are screening thousands of chemicals in HTS in vitro assays for a broad range of targets (Dix et al. 2007; Judson et al. 2010; Martin et al. 2010).”
Self-citationComputational toxicology in drug development
Muster, W., Breidenbach, A., Fischer, H., Kirchner, S., Müller, L., & Pähler, A. (2008) · Drug Discovery Today · doi:10.1016/j.drudis.2007.12.007 · Cited by 215 · cited 2×
“These structure-based methods are particularly appealing for their ability to predict toxi-cologically relevant end points quickly and at low cost (Muster et al. 2008; Vedani and Smiesko 2009).”
ACToR — Aggregated Computational Toxicology Resource
Judson, R., Richard, A., Dix, D. J., Houck, K. A., Elloumi, F., Martin, M. T., Cathey, T., Transue, T. R., Spencer, R. M., & Wolf, M. A. (2008) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2007.12.037 · Cited by 214 · cited 1×
“Available in the U.S. EPA chemical product categories database (CPCat), which is part of the Aggregated Computational Toxicology Resource (ACToR) system (Dionisio et al. 2015; Judson et al. 2008, 2012; U.S. EPA 2014a).”
Self-citationISIDA - Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors
Varnek, A. A., Fourches, D., Horvath, D., Klimchuk, O., Gaudin, C., Vayer, P., Solov’ev, V. P., Hoonakker, F., Tetko, I. V., & Marcou, G. (2008) · Current Computer - Aided Drug Design · doi:10.2174/157340908785747465 · Cited by 213 · cited 5×
“ISIDA - Platform for virtual screening based on fragment and pharmaco phoric descriptors.”
MethodInfluentialSelf-citationJudson, R., Kavlock, R. J., Setzer, R. W., Hubal, E. A. C., Martin, M. T., Knudsen, T. B., Houck, K. A., Thomas, R. S., Wetmore, B. A., & Dix, D. J. (2011) · Chemical Research in Toxicology · doi:10.1021/tx100428e · Cited by 204 · cited 1×
“Recently, in vitro high-throughput screening (HTS) assays have emerged and become a viable tool for large-scale chemical testing (Judson et al. 2011; Kavlock and Dix 2010; Wetmore et al. 2012).”
Self-citationThe exposure data landscape for manufactured chemicals
Egeghy, P. P., Judson, R., Gangwal, S., Mosher, S., Smith, D., Vail, J., & Hubal, E. A. C. (2011) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2011.10.046 · Cited by 200 · cited 2×
“Consequently, toxicological data and especially estrogenic activity data are available only for a limited number of compounds (Cohen Hubal et al. 2010; Egeghy et al. 2012; Judson et al. 2009).”
Self-citationKowalski, B. R., & Bender, C. F. (1972) · Analytical Chemistry · doi:10.1021/ac60316a008 · Cited by 191 · cited 1×
“…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 Bayes classifier…”
Distributed structure-searchable toxicity (DSSTox) public database network: a proposal
Richard, A. M., & Williams-DeVane, C. (2002) · Mutation research. Fundamental and molecular mechanisms of mutagenesis · doi:10.1016/s0027-5107(01)00289-5 · Cited by 170
Self-citationHuang, R., Sakamuru, S., Martin, M. T., Reif, D. M., Judson, R., Houck, K. A., Casey, W., Hsieh, J., Shockley, K. R., Ceger, P. C., et al. (2014) · Scientific Reports · doi:10.1038/srep05664 · Cited by 169 · cited 4×
“The data that were suggested to be used by the participants as a training set to develop and optimize their models was derived from ToxCastTM and Tox21 programs (Dix et al. 2007; Huang et al. 2014; Judson et al. 2010).”
InfluentialSelf-citationUsing in Vitro High Throughput Screening Assays to Identify Potential Endocrine-Disrupting Chemicals
Rotroff, D. M., Dix, D. J., Houck, K. A., Knudsen, T. B., Martin, M. T., McLaurin, K. W., Reif, D. M., Crofton, K. M., Singh, A. V., Xia, M., et al. (2012) · Environmental Health Perspectives · doi:10.1289/ehp.1205065 · Cited by 164 · cited 1×
“hormone receptors (Jacobs et al. 2008; Rotroff et al. 2013; Shanle and Xu 2011; Sung et al. 2012).”
Self-citationBodor, N. S., Huang, M., & Harget, A. J. (1994) · Journal of Molecular Structure THEOCHEM · doi:10.1016/0166-1280(94)80078-2 · Cited by 163
Estrogen receptors and endocrine diseases: lessons from estrogen receptor knockout mice
Mueller, S. O. (2001) · Current Opinion in Pharmacology · doi:10.1016/s1471-4892(01)00105-9 · Cited by 157 · 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).”
InfluentialKavlock, R., & Dix, D. (2010) · Journal of Toxicology and Environmental Health. Part B, Critical Reviews · doi:10.1080/10937404.2010.483935 · Cited by 157 · cited 1×
“Recently, in vitro high-throughput screening (HTS) assays have emerged and become a viable tool for large-scale chemical testing (Judson et al. 2011; Kavlock and Dix 2010; Wetmore et al. 2012).”
LeadScope: Software for Exploring Large Sets of Screening Data
Roberts, G., Myatt, G. J., Johnson, W. P., Cross, K. P., & Blower, P. E. (2000) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0000631 · Cited by 155
KNIME: The Konstanz Information Miner
Berthold, M. R., Cebron, N., Dill, F., Fatta, G. D., Gabriel, T. R., Georg, F., Meinl, T., Ohl, P., Sieb, C., & Wiswedel, B. (2006) · CentAUR (University of Reading) · Cited by 151
Neural Network Studies. 4. Introduction to Associative Neural Networks
Tetko, I. V. (2002) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci010379o · Cited by 150
Self-citationExploring consumer exposure pathways and patterns of use for chemicals in the environment
Dionisio, K. L., Frame, A. M., Goldsmith, M., Wambaugh, J. F., Liddell, A. C., Cathey, T., Smith, D., Vail, J., Ernstoff, A. S., Fantke, P., et al. (2015) · Toxicology Reports · doi:10.1016/j.toxrep.2014.12.009 · Cited by 137 · cited 2×
“There are tens of thousands of natural and synthetic chemical substances to which humans and wildlife are exposed (Dionisio et al. 2015; Egeghy et al. 2012; Judson et al. 2009).”
Self-citationEnvironmental Estrogens linked to Reproductive Abnormalities, Cancer
HILEMAN, Β. (1994) · Chemical & Engineering News · doi:10.1021/cen-v072n005.p019 · Cited by 120 · cited 1×
“This concern about estrogen-like activity of man-made chemicals is because of their potential for negatively affecting reproductive function (Hileman 1994; Kavlock et al. 1996).”
Judson, R., Martin, M. T., Egeghy, P. P., Gangwal, S., Reif, D. M., Kothiya, P., Wolf, M. A., Cathey, T., Transue, T. R., Smith, D., et al. (2012) · International Journal of Molecular Sciences · doi:10.3390/ijms13021805 · Cited by 118 · cited 1×
“Available in the U.S. EPA chemical product categories database (CPCat), which is part of the Aggregated Computational Toxicology Resource (ACToR) system (Dionisio et al. 2015; Judson et al. 2008, 2012; U.S. EPA 2014a).”
Self-citationA Curated Database of Rodent Uterotrophic Bioactivity
Kleinstreuer, N., Ceger, P. C., Allen, D., Strickland, J. A., Chang, X., Hamm, J. T., & Casey, W. (2015) · Environmental Health Perspectives · doi:10.1289/ehp.1510183 · Cited by 118 · cited 1×
“Uncertainty and data discordance was also reported in literature review of in vivo uterotrophic bioassays (Kleinstreuer et al. 2015).”
Advancing Exposure Characterization for Chemical Evaluation and Risk Assessment
Hubal, E. A. C., Richard, A., Aylward, L. L., Edwards, S., Gallagher, J. E., Goldsmith, M., Isukapalli, S., Tornero‐Velez, R., Weber, E., & Kavlock, R. J. (2010) · Journal of Toxicology and Environmental Health Part B · doi:10.1080/10937404.2010.483947 · Cited by 103 · cited 1×
“Consequently, toxicological data and especially estrogenic activity data are available only for a limited number of compounds (Cohen Hubal et al. 2010; Egeghy et al. 2012; Judson et al. 2009).”
Self-citationQNA-based ‘Star Track’ QSAR approach
Filimonov, D. A., Zakharov, A. V., Lagunin, A. A., & Poroikov, V. (2009) · SAR and QSAR in environmental research · doi:10.1080/10629360903438370 · Cited by 99 · cited 3×
“…QikProp (version 3.4, http:// www.schrodinger.com/QikProp/), multilevel and quantitative neighborhoods of atoms (MNA, QNA) used by GUSAR and PASS (Filimonov et al. 2009; Poroikov et al. 2000), DRAGON (Talete srl 2012), Mold2 (Hong et al. 2008, 2012), GLIDE (version 6.5, http://…”
InfluentialSelf-citationHow Accurately Can We Predict the Melting Points of Drug-like Compounds?
Tetko, I. V., Sushko, Y., Novotarskyi, S., Patiny, L., Kondratov, I. S., Petrenko, A. E., Charochkina, L., & Asiri, A. M. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci5005288 · Cited by 96 · cited 2×
“…Bayes classifier (Poroikov et al. 2000), self-consistent regression with radial basis function interpolation (RBF-SCR) (Zakharov et al. 2014), OCHEM machine learning methods (Tetko et al. 2014), docking and consensus of different approaches (Horvath et al. 2014; Ng et al. 2014; Sushko et al. 2011).”
MethodSelf-citationNg, H. W., Zhang, W., Shu, M., Luo, H., Ge, W., Perkins, R., Tong, W., & Hong, H. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-s11-s4 · Cited by 85 · cited 3×
“…Bayes classifier (Poroikov et al. 2000), self-consistent regression with radial basis function interpolation (RBF-SCR) (Zakharov et al. 2014), OCHEM machine learning methods (Tetko et al. 2014), docking and consensus of different approaches (Horvath et al. 2014; Ng et al. 2014; Sushko et al. 2011).”
Self-citationTetko, I. V. (2002) · Neural Processing Letters · doi:10.1023/a:1019903710291 · Cited by 83
Self-citationEADB: An Estrogenic Activity Database for Assessing Potential Endocrine Activity
Shen, J., Xu, L., Fang, H., Richard, A. M., Bray, J. D., Judson, R., Zhou, G., Colatsky, T. J., Aungst, J. L., Teng, C. T., et al. (2013) · Toxicological Sciences · doi:10.1093/toxsci/kft164 · Cited by 82
Self-citationTautomerism in large databases
Sitzmann, M., Ihlenfeldt, W., & Nicklaus, M. C. (2010) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-010-9346-4 · Cited by 80
Self-citationAnin silicoensemble method for lead discovery: decision forest
Hong, H., Tong, W., Xie, Q., Fang, H., & Perkins, R. (2005) · SAR and QSAR in environmental research · doi:10.1080/10659360500203022 · Cited by 71 · cited 1×
“…1987; Wold et al. 2001), partial least-squares discriminant analysis (PLS-DA) (Frank and Friedman 1993; Nouwen et al. 1997), decision forest (DF) (Hong et al. 2005, 2004; Tong et al. 2003; Xie et al. 2005), three-dimensional (3D) quantitative spectral data–activity relationship (QSDAR) (Beger et…”
Self-citationZang, Q., Rotroff, D. M., & Judson, R. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400527b · Cited by 69
Self-citationMahoney, M. M., & Padmanabhan, V. (2010) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2010.05.017 · Cited by 68 · cited 1×
“A subset of these compounds may disrupt normal functioning of the endocrine system and cause health hazards to both humans and ecological species (Birnbaum and Fenton 2003; Diamanti-Kandarakis et al. 2009; Mahoney and Padmanabhan 2010; UNEP and WHO 2013).”
The structure-activity relationship in amides inhibiting photosynthesis
Hansch, C. H., & Deutsch, E. W. (1966) · Biochimica et Biophysica Acta (BBA) - Biophysics including Photosynthesis · doi:10.1016/0926-6585(66)90242-1 · Cited by 64 · cited 1×
“QSARs have been promoted and their use recognized since the pioneering work of Hansch in the 1960s (Fujita et al. 1964; Hansch et al. 1962; Hansch and Deutsch 1966).”
An Evolutionary Optimizer of libsvm Models
Horvath, D., Brown, J. B., Marcou, G., & Varnek, A. A. (2014) · Challenges · doi:10.3390/challe5020450 · Cited by 63 · cited 3×
“2015) Docking GLIDE score ToxCastTM (1,677) Categorical UNIMIB kNN DRAGON + fingerprints ToxCastTM (1,677) Categorical UNISTRA (Horvath et al. 2014) SVM ISIDA ToxCastTM (1,529) Categorical + continuous”
Self-citationThe Use of Metabolising Systems for In Vitro Testing of Endocrine Disruptors
Jacobs, M. N., Janssens, W., Bernauer, U., Brandon, E. F. A., Coecke, S., Combes, R. D., Edwards, P. M., Freidig, A. P., Freyberger, A., Kolanczyk, R. C., et al. (2008) · Current Drug Metabolism · doi:10.2174/138920008786049294 · Cited by 61 · cited 1×
“…in vitro and in vivo protocols to identify potential endocrine pathway-mediated effects of chemicals, including interactions with hormone receptors (Jacobs et al. 2008; Rotroff et al. volume 124 | number 7 | July 2016 • Environmental Health Perspectives 2013; Shanle and Xu 2011; Sung et al. 2012).”
Xie, Q., Ratnasinghe, L. D., Hong, H., Perkins, R., Tang, Z., Hu, N., Taylor, P. R., & Tong, W. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-s2-s4 · Cited by 61
Self-citationIn Silico Toxicology in Drug Discovery — Concepts Based on Three-dimensional Models
Vedani, A., & Smieško, M. (2009) · Alternatives to Laboratory Animals · doi:10.1177/026119290903700506 · Cited by 60
A New Approach to Radial Basis Function Approximation and Its Application to QSAR
Zakharov, A. V., Peach, M. L., Sitzmann, M., & Nicklaus, M. C. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci400704f · Cited by 56
Self-citationBinary classification models for endocrine disrupter effects mediated through the estrogen receptor
Roncaglioni, A., Piclin, N., Pintore, M., & Benfenati, E. (2008) · SAR and QSAR in environmental research · doi:10.1080/10629360802550606 · Cited by 47
Self-citationTaha, M. O., Tarairah, M., Zalloum, H. M., & Abu-Sheikha, G. (2009) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2009.09.005 · Cited by 44 · 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.”
Hong, H., Tong, W., Perkins, R., Fang, H., Xie, Q., & Shi, L. M. (2004) · DNA and Cell Biology · doi:10.1089/1044549042476839 · Cited by 41
Self-citationTrisciuzzi, D., Alberga, D., Mansouri, K., Judson, R., Cellamare, S., Catto, M., Carotti, A., Benfenati, E., Novellino, E., Mangiatordi, G. F., et al. (2015) · Future Medicinal Chemistry · doi:10.4155/fmc.15.103 · Cited by 39 · cited 2×
“2010) Categorical UNC_MML SVM+RF DRAGON ToxCastTM (120) Categorical UNIBA (Trisciuzzi et al. 2015) Docking GLIDE score ToxCastTM (1,677) Categorical UNIMIB kNN DRAGON + fingerprints ToxCastTM (1,677) Categorical UNISTRA (Horvath et al.”
InfluentialSelf-citationRybacka, A., Rudén, C., Tetko, I. V., & Andersson, P. L. (2015) · Chemosphere · doi:10.1016/j.chemosphere.2015.07.036 · Cited by 36
Self-citationBeger, R. D., & Wilkes, J. G. (2001) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1011959120313 · Cited by 30 · cited 2×
“Models were developed using both well-known and innovative methods including partial least-squares (PLS) (Ståhle and Wold 1987; Wold et al. 2001), partial least-squares discriminant analysis (PLS-DA) (Frank and Friedman 1993; Nouwen et al. 1997), decision forest (DF) (Hong et al. 2005, 2004; Tong et al. 2003; Xie et a…”
Self-citationBeger, R. D., Buzatu, D. A., Wilkes, J. G., & Lay, J. O. (2001) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci010285e · Cited by 28 · cited 3×
“2005), three-dimensional (3D) quantitative 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 neu…”
Self-citationNouwen, J., Lindgren, F., Hansen, B., Karcher, W., Verhaar, H. J. M., & Hermens, J. L. M. (1997) · Environmental Science & Technology · doi:10.1021/es9609213 · Cited by 22 · cited 3×
“…partial least-squares (PLS) (Ståhle and Wold 1987; Wold et al. 2001), partial least-squares discriminant analysis (PLS-DA) (Frank and Friedman 1993; Nouwen et al. 1997), decision forest (DF) (Hong et al. 2005, 2004; Tong et al. 2003; Xie et al. 2005), three-dimensional (3D) quantitative spectral…”
Steinmetz, F. P., Enoch, S. J., Madden, J. C., Nelms, M., Rodríguez-Sánchez, N., Rowe, P., Wen, Y., & Cronin, M. (2014) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2014.02.115 · Cited by 17
Complementary PLS and KNN algorithms for improved 3D-QSDAR consensus modeling of AhR binding
Slavov, S. H., Pearce, B. A., Buzatu, D. A., Wilkes, J. G., & Beger, R. D. (2013) · Journal of Cheminformatics · doi:10.1186/1758-2946-5-47 · Cited by 13
Self-citationRivas, G., Tangemann, K., Minton, A. P., & Engel, J. (1996) · Journal of Molecular Recognition · doi:10.1002/(sici)1099-1352(199601)9:1 · Cited by 11
Detection of endocrine disruptors – from simple assays to whole genome scanning
Sung, E., Turan, N., Ho, P. W., Ho, S. ‐., Jarratt, P. D. B., Waring, R. H., & Ramsden, D. B. (2012) · International Journal of Andrology · doi:10.1111/j.1365-2605.2012.01254.x · Cited by 11 · cited 1×
“…in vitro and in vivo protocols to identify potential endocrine pathway-mediated effects of chemicals, including interactions with hormone receptors (Jacobs et al. 2008; Rotroff et al. volume 124 | number 7 | July 2016 • Environmental Health Perspectives 2013; Shanle and Xu 2011; Sung et al. 2012).”
Mold2Molecular Descriptors for QSAR
Hong, H., Slavov, S. H., Ge, W., Qian, F., Su, Z., Fang, H., Cheng, Y., Perkins, R., Shi, L. M., & Tong, W. (2012) · Wiley eBooks · doi:10.1002/9783527645121.ch3 · Cited by 9 · cited 2×
“…neighborhoods of atoms (MNA, QNA) used by GUSAR and PASS (Filimonov et al. 2009; Poroikov et al. 2000), DRAGON (Talete srl 2012), Mold2 (Hong et al. 2008, 2012), GLIDE (version 6.5, http:// www.schrodinger.com/Glide), AutoDock (Goodsell et al. 1996), ISIDA (Varnek et al. 2008), and…”
InfluentialSelf-citation
Flagged first, then by citations
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
Cited by
Sorted by citations
Machine Learning: New Ideas and Tools in Environmental Science and Engineering
Zhong, S., Zhang, K., Bagheri, M., Burken, J. G., Gu, A. Z., Li, B., Ma, X., Marrone, B. L., Ren, Z. J., Schrier, J., et al. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.1c01339 · Cited by 999
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
Self-citationToxCast Chemical Landscape: Paving the Road to 21st Century Toxicology
Richard, A. M., Judson, R., Houck, K. A., Grulke, C., Volarath, P., Thillainadarajah, I., Yang, C., Rathman, J. F., Martin, M. T., Wambaugh, J. F., et al. (2016) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.6b00135 · Cited by 714
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 650
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 401
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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