Publicly available QSPR models for environmental media persistence

References

• T. Junker, A. Coors, and G. Schüürmann, Compartment-specific screening tools for persistence: Potential role and application in the regulatory context, Integr. Environ. Assess. Manag. 15 (2019), pp. 470–481. doi:10.1002/ieam.4125.
   PubMed Web of Science ®Google Scholar
• R.J. Larson and C.E. Cowan, Quantitative application of biodegradation data to environmental risk and exposure assessments, Environ. Toxicol. Chem. 14 (1995), pp. 1433–1442. doi:10.1002/etc.5620140821.
   Web of Science ®Google Scholar
• ECETOC, Persistence of chemicals in the environment, Tech. Rep 90, European Centre for Ecotoxicology and Toxicology of Chemicals, Brussels, BE, 2003.
   Google Scholar
• European Commission, Regulation (EC) no 1907/2006 of the European parliament and of the council of 18 December 2006 concerning the registration, evaluation, authorisation and restriction ofchemicals (REACH), establishing a European chemicals agency, amending directive 1999/45/ECC and repealing council regulation (EEC) No 793/93 and commission regulation (EC) No 1488/94 as well as council directive 76/769/EEC and commission directives 91/155/EEC, 93/67/EEC, 93/105/EEC and 2000/21/EC, Off. J. Eur. Union 50 (2007), pp. 1–281.
   Google Scholar
• ECHA, Guidance on information requirements and chemical safety assessment, R.11: PBT/vPvB assessment, Tech. Rep. ED-01-17-294, European Chemicals Agency, Helsinki, FI, 2017.
   Google Scholar
• F. Lunghini, G. Marcou, P. Gantzer, P. Azam, D. Horvath, E. Van Miert, and A. Varnek, Modelling of ready biodegradability based on combined public and industrial data sources, SAR QSAR Environ. Res. 31 (2020), pp. 171–186. doi:10.1080/1062936X.2019.1697360.
   PubMed Web of Science ®Google Scholar
• OECD, Test No. 301: Ready biodegradability, Tech. Rep. 9789264070349, Organisation for Economic Co-operation Development, Paris, FR, 1992.
   Google Scholar
• OECD, OECD guidelines for the testing of chemicals, section 3: Environmental fate and behaviour, Organisation for Economic Cooperation and Development, Paris, FR, 2019. Available at http://www.oecd.org/env/ehs/testing/oecdguidelinesforthetestingofchemicals.htm.
   Google Scholar
• D.A.R.S. Latino, J. Wicker, M. Gütlein, E. Schmid, S. Kramer, and K. Fenner, Eawag-soil in envipath: A new resource for exploring regulatory pesticide soil biodegradation pathways and half-life data, Environ. Sci. Process. Impacts 19 (2017), pp. 449–464. doi:10.1039/C6EM00697C.
   PubMed Web of Science ®Google Scholar
• M. Pavan and A.P. Worth, Review of estimation models for biodegradation, QSAR Comb. Sci. 27 (2008), pp. 32–40. doi:10.1002/qsar.200710117.
   Google Scholar
• K. Acharya, D. Werner, J. Dolfing, M. Barycki, P. Meynet, W. Mrozik, O. Komolafe, T. Puzyn, and R.J. Davenport, A quantitative structure-biodegradation relationship (QSBR) approach to predict biodegradation rates of aromatic chemicals, Water Res. 157 (2019), pp. 181–190. doi:10.1016/j.watres.2019.03.086.
   PubMed Web of Science ®Google Scholar
• M. Salahinejad, E. Zolfonoun, and J.B. Ghasemi, Predicting degradation half-life of organophosphorus pesticides in soil using three-dimensional molecular interaction fields, Int. J. Quant. Struct. Relat. 2 (2017), pp. 27–35.
   Google Scholar
• K. Samghani and M. HosseinFatemi, Developing a support vector machine based QSPR model for prediction of half-life of some herbicides, Ecotoxicol. Environ. Saf. 129 (2016), pp. 10–15. doi:10.1016/j.ecoenv.2016.03.002.
   PubMed Web of Science ®Google Scholar
• K. Mansouri, C.M. Grulke, R.S. Judson, and A.J. Williams, OPERA models for predicting physicochemical properties and environmental fate endpoints, J. Cheminform. 10 (2018), pp. 1–19. doi:10.1186/s13321-018-0263-1.
   PubMed Web of Science ®Google Scholar
• F. Pizzo, A. Lombardo, M. Brandt, A. Manganaro, and E. Benfenati, A new integrated in silico strategy for the assessment and prioritization of persistence of chemicals under REACH, Environ. Int. 88 (2016), pp. 250–260. doi:10.1016/j.envint.2015.12.019.
   PubMed Web of Science ®Google Scholar
• E. Benfenati, A. Manganaro, and G. Gini, VEGA-QSAR: AI inside a platform for predictive toxicology. Proceedings of the workshop ‘Popularize Artificial Intelligence 2013, December 5th 2013, Turin, Italy, 2013. published on CEUR Workshop Proceedings Vol 1107.
   Google Scholar
• US EPA, Estimation Programs Interface Suite™ for Microsoft® Windows V4.11, US Environmental Protection Agency, Washington DC, 2012; software available at https://www.epa.gov/tsca- screening-tools/epi-suitetm-estimation-program-interface.
   Google Scholar
• G. Marcou, D. Horvath, F. Bonachera, and A. Varnek, Laboratoire de Chemoinformatique UMR 7140 CNRS, University of Strasbourg, Strasbourg, FR, 2019. available at http://infochim. u-strasbg.fr/.
   Google Scholar
• RIVM, Pesticides: Benefaction or Pandora’s Box? A synopsis of the environmental aspects of 243 pesticides, Tech. Rep. 679101014, National Institute of Public Health and Environmental Protection, Bilthoven, NL, 1994.
   Google Scholar
• NPIC, Data from: OSU extension pesticide properties database. National Pesticide Information Center; dataset available at http://npic.orst.edu/ingred/ppdmove.htm.
   Google Scholar
• OECD, Data from: EChemPortal: Global portal to information on chemical substances, Organisation for Economic Co-operation Development; dataset available at https://www. echemportal.org/echemportal/index.action.
   Google Scholar
• T. Gouin, I. Cousins, and D. Mackay, Comparison of two methods for obtaining degradation half-lives, Chemosphere 56 (2004), pp. 531–535. doi:10.1016/j.chemosphere.2004.04.018.
   PubMed Web of Science ®Google Scholar
• P. Gramatica and E. Papa, Screening and ranking of pops for global half-life: QSAR approaches for prioritization based on molecular structure, Environ. Sci. Technol. 41 (2007), pp. 2833–2839. doi:10.1021/es061773b.
   PubMed Web of Science ®Google Scholar
• UH, Data from: PPDB: Pesticide properties database. Agriculture & Environment Research Unit (AERU), University of Hertfordshire.
   Google Scholar
• M. Berthold, N. Cebron, F. Dill, T. Gabriel, T. Kötter, T. Meinl, P. Ohl, K. Thiel, and B. Wiswedel, KNIME - the konstanz information miner: Version 2.0 and beyond, SIGKDD Explor. 11 (2009), pp. 26–31. doi:10.1145/1656274.1656280.
   Google Scholar
• F. Ruggiu, G. Marcou, A. Varnek, and D. Horvath, ISIDA property-labelled fragment descriptors, Mol. Inform. 29 (2010), pp. 855–868. doi:10.1002/minf.201000099.
   PubMed Web of Science ®Google Scholar
• N. Kireeva, I.I. Baskin, H.A. Gaspar, D. Horvath, G. Marcou, and A. Varnek, Generative topographic mapping (GTM): Universal tool for data visualization, structure-activity modeling and dataset comparison, Mol. Inform. 31 (2012), pp. 301–312. doi:10.1002/minf.201100163.
   PubMed Web of Science ®Google Scholar
• C.M. Bishop, M. Svensén, C.K.I. Williams, and M. Svens, The generative topographic mapping, Neural. Comput. 10 (1998), pp. 215–234. doi:10.1162/089976698300017953.
   Web of Science ®Google Scholar
• D. Horvath, J. Brown, G. Marcou, and A. Varnek, An evolutionary optimizer of libsvm models, Challenges 5 (2014), pp. 450–472. doi:10.3390/challe5020450.
   Google Scholar
• C. Chang and C.-J. Lin, LIBSVM: A library for support vector machines, ACM Tran. Int. Syst. Technol. 2 (3) (2011), pp. 1–27. doi:10.1145/1961189.1961199.
   Web of Science ®Google Scholar
• I.H. Witten, E. Frank, M.A. Hall, and C.J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, 4th ed., Morgan Kaufmann, San Fransisco CA, 2016.
   Google Scholar
• A. Tropsha, P. Gramatica, and V.K. Gombar, The importance of being Earnest: Validation is the absolute essential for successful application and interpretation of QSPR models, QSAR Comb. Sci. 22 (2003), pp. 69–77. doi:10.1002/qsar.200390007.
   Google Scholar
• F. Lunghini, G. Marcou, P. Azam, R. Patoux, M.H. Enrici, F. Bonachera, D. Horvath, and A. Varnek, QSPR models for bioconcentration factor (BCF): Are they able to predict data of industrial interest? SAR QSAR Environ. Res. 30 (2019), pp. 507–524. doi:10.1080/1062936X.2019.1626278.
   PubMed Web of Science ®Google Scholar
• F. Lunghini, G. Marcou, P. Azam, D. Horvath, R. Patoux, E. Van Miert, and A. Varnek, Consensus models to predict oral rat acute toxicity and validation on a dataset coming from the industrial context, SAR QSAR Environ. Res. 30 (2019), pp. 879–897. doi:10.1080/1062936X.2019.1672089.
   PubMed Web of Science ®Google Scholar
• G. Marcou, D. Horvath, V. Solov’Ev, A. Arrault, P. Vayer, and A. Varnek, Interpretability of SAR/QSAR models of any complexity by atomic contributions, Mol. Inform. 31 (2012), pp. 639–642. doi:10.1002/minf.201100136.
   PubMed Web of Science ®Google Scholar
• R.S. Boethling, E. Sommer, and D. DiFiore, Designing small molecules for biodegradability, Chem. Rev. 107 (2007), pp. 2207–2227. doi:10.1021/cr050952t.
   PubMed Web of Science ®Google Scholar
• E. Papa and P. Gramatica, Screening of persistent organic pollutants by QSBR classification models: A comparative study, J. Mol. Graph. Model. 27 (2008), pp. 59–65. doi:10.1016/j.jmgm.2008.02.004.
   PubMed Web of Science ®Google Scholar
• S. Abeyruwan, U.D. Vempati, H. Küçük-McGinty, U. Visser, A. Koleti, A. Mir, K. Sakurai, C. Chung, J.A. Bittker, P.A. Clemons, S. Brudz, A. Siripala, A.J. Morales, M. Romacker, D. Twomey, S. Bureeva, V. Lemmon, and S.C. Schürer, Evolving BioAssay Ontology (BAO): Modularization, integration and applications, J. Biomed. Semant. 5 (2014), pp. S5. doi:10.1186/2041-1480-5-S1-S5.
   PubMed Web of Science ®Google Scholar