Relación cuantitativa estructura actividad del factor de bioconcentración de los bifenilos policlorados en especies de peces utilizando métodos basados en aprendizaje de máquina Relación cuantitativa estructura actividad del factor de bioconcentración de los bifenilos policlorados

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Los bifenilos policlorados (PCBs) son contaminantes persistentes que afectan enormemente a los ecosistemas marinos. Utilizando técnicas de aprendizaje de máquina, se construyeron modelos de relación cuantitativa estructura-actividad (RCEA) para predecir el factor de bioconcentración (BCF) de los PCBs. Estos modelos se construyeron a partir de descriptores topográficos 2D y 3D calculados para la estructura molecular optimizada en el nivel de mecánica molecular. Después de analizar sus parámetros estadísticos, se determinó que dos modelos son bastante robustos para la predicción de logBCF. Los modelos seleccionados fueron: M_4_LR construido con dos descriptores moleculares y presenta valores de r2 = 0,9154, Q2LOO = 0,8944, y Q2ext = 0,9119, y M_13 construido con cuatro descriptores moleculares y presenta valores de r2 = 0,9375, Q2LOO = 0,9155, y Q2ext = 0,844. Los dos modelos pasaron la doble fase de validación y cumplieron con los criterios de la prueba de Tropsha. Esto implica que las predicciones para el logBCF fueron bastante precisas tal como se muestra en los resultados del presente estudio.

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Mora, J. R. (2021). Relación cuantitativa estructura actividad del factor de bioconcentración de los bifenilos policlorados en especies de peces utilizando métodos basados en aprendizaje de máquina: Relación cuantitativa estructura actividad del factor de bioconcentración de los bifenilos policlorados. ACI Avances En Ciencias E Ingenierías, 13(2), 19. https://doi.org/10.18272/aci.v13i2.2275
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1. Santos, L. L., Miranda, D., Hatje, V., Albergaria-Barbosa, A. C. R., & Leonel, J. (2020). PCBs occurrence in marine bivalves and fish from Todos os Santos Bay, Bahia, Brazil. Marine Pollution Bulletin, 154, 111070. https://doi.org/10.1016/j.marpolbul.2020.111070
2. Ai, H., Wu, X., Zhang, L., Qi, M., Zhao, Y., Zhao, Q., Zhao, J., & Liu, H. (2019). QSAR modelling study of the bioconcentration factor and toxicity of organic compounds to aquatic organisms using machine learning and ensemble methods. Ecotoxicology and Environmental Safety, 179, 71–78. https://doi.org/10.1016/j.ecoenv.2019.04.035
3. Bartalini, A., Muñoz-Arnanz, J., Baini, M., Panti, C., Galli, M., Giani, D., Fossi, M. C., & Jiménez, B. (2020). Relevance of current PCB concentrations in edible fish species from the Mediterranean Sea. Science of The Total Environment, 737, 139520. https://doi.org/10.1016/j.scitotenv.2020.139520
4. Soni, A. K., Sahu, V. K., & Sahu, S. (2017). DFT-Based Prediction of Bioconcentration Factors of Polychlorinated Biphenyls in Fish Species Using Atomic Descriptors. Asian Journal of Chemistry, 29(11), 2515–2521. https://doi.org/10.14233/ajchem.2017.20839
5. Zhang, R., Kang, Y., Yu, K., Han, M., Wang, Y., Huang, X., Ding, Y., Wang, R., & Pei, J. (2021). Occurrence, distribution, and fate of polychlorinated biphenyls (PCBs) in multiple coral reef regions from the South China Sea: A case study in spring-summer. Science of The Total Environment, 777, 146106. https://doi.org/10.1016/j.scitotenv.2021.146106
6. Safe, S. H. (1994). Polychlorinated Biphenyls (PCBs): Environmental Impact, Biochemical and Toxic Responses, and Implications for Risk Assessment. Critical Reviews in Toxicology, 24(2), 87–149. https://doi.org/10.3109/10408449409049308
7. Lunghini, F., Marcou, G., Azam, P., Enrici, M. H., Van Miert, E., & Varnek, A. (2020). Publicly available QSPR models for environmental media persistence. SAR and QSAR in Environmental Research, 31(7), 493–510. https://doi.org/10.1080/1062936X.2020.1776387
8. Liu, H., Liu, H., Sun, P., & Wang, Z. (2014). QSAR studies of bioconcentration factors of polychlorinated biphenyls (PCBs) using DFT, PCS and CoMFA. Chemosphere, 114, 101–105. https://doi.org/10.1016/j.chemosphere.2014.03.113
9. Devriese, L. I., De Witte, B., Vethaak, A. D., Hostens, K., & Leslie, H. A. (2017). Bioaccumulation of PCBs from microplastics in Norway lobster (Nephrops norvegicus): An experimental study. Chemosphere, 186, 10–16. https://doi.org/10.1016/j.chemosphere.2017.07.121
10. Yeo, B. G., Takada, H., Yamashita, R., Okazaki, Y., Uchida, K., Tokai, T., Tanaka, K., & Trenholm, N. (2020). PCBs and PBDEs in microplastic particles and zooplankton in open water in the Pacific Ocean and around the coast of Japan. Marine Pollution Bulletin, 151, 110806. https://doi.org/10.1016/j.marpolbul.2019.110806
11. Soni, A. K., Singh, P., & Sahu, V. K. (2020). DFT-Based Prediction of Bioconcentration Factors of Polychlorinated Biphenyls in Fish Species Using Molecular Descriptors. Advances in Biological Chemistry, 10(01), 1–15. https://doi.org/10.4236/abc.2020.101001
12. Mikolajczyk, S., Warenik-Bany, M., Maszewski, S., & Pajurek, M. (2020). Dioxins and PCBs – Environment impact on freshwater fish contamination and risk to consumers. Environmental Pollution, 263, 114611. https://doi.org/10.1016/j.envpol.2020.114611
13. Gad, S. C. (2005). Toxicity Testing, Aquatic. En P. Wexler (Ed.), Encyclopedia of Toxicology (Second Edition) (pp. 233–239). Elsevier. https://doi.org/10.1016/B0-12-369400-0/00963-7
14. Schmitz, K. S. (2018). Chapter 4—Life Science. En K. S. Schmitz (Ed.), Physical Chemistry (pp. 755–832). Elsevier. https://doi.org/10.1016/B978-0-12-800513-2.00004-8
15. Peake, B. M., Braund, R., Tong, A. Y. C., & Tremblay, L. A. (2016). 5—Impact of pharmaceuticals on the environment. En B. M. Peake, R. Braund, A. Y. C. Tong, & L. A. Tremblay (Eds.), The Life-Cycle of Pharmaceuticals in the Environment (pp. 109–152). Woodhead Publishing. https://doi.org/10.1016/B978-1-907568-25-1.00005-0
16. Lunghini, F., Marcou, G., Azam, P., Patoux, R., Enrici, M. H., Bonachera, F., Horvath, D., & Varnek, A. (2019). QSPR models for bioconcentration factor (BCF): Are they able to predict data of industrial interest? SAR and QSAR in Environmental Research, 30(7), 507–524. https://doi.org/10.1080/1062936X.2019.1626278
17. Marigómez, I. (2014). Environmental Risk Assessment, Marine. En P. Wexler (Ed.), Encyclopedia of Toxicology (Third Edition) (pp. 398–401). Academic Press. https://doi.org/10.1016/B978-0-12-386454-3.00556-X
18. Silakari, O., & Singh, P. K. (2021). Chapter 2 - QSAR: Descriptor calculations, model generation, validation and their application. En O. Silakari & P. K. Singh (Eds.), Concepts and Experimental Protocols of Modelling and Informatics in Drug Design (pp. 29–63). Academic Press. https://doi.org/10.1016/B978-0-12-820546-4.00002-7
19. 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., Isayev, O., Curtalolo, S., Fourches, D., Cohen, Y., Aspuru-Guzik, A., Winkler, D. A., Agrafiotis, D., Cherkasov, A., & Tropsha, A. (2020). QSAR without borders. Chemical Society Reviews, 49(11), 3525–3564. https://doi.org/10.1039/D0CS00098A
20. Chandrasekaran, B., Abed, S. N., Al-Attraqchi, O., Kuche, K., & Tekade, R. K. (2018). Chapter 21—Computer-Aided Prediction of Pharmacokinetic (ADMET) Properties. En R. K. Tekade (Ed.), Dosage Form Design Parameters (pp. 731–755). Academic Press. https://doi.org/10.1016/B978-0-12-814421-3.00021-X
21. Gund, T. (1996). 3—Molecular Modeling of Small Molecules. En N. C. Cohen (Ed.), Guidebook on Molecular Modeling in Drug Design (pp. 55–92). Academic Press. https://doi.org/10.1016/B978-012178245-0/50004-4
22. Errol G. Lewars. (2011). Computational Chemistry: Introduction to the Theory and Applications of Molecular and Quantum Mechanics (2a ed.). Springer Netherlands.
23. Tosco, P., Stiefl, N., & Landrum, G. (2014). Bringing the MMFF force field to the RDKit: Implementation and validation. Journal of Cheminformatics, 6(1), 37. https://doi.org/10.1186/s13321-014-0037-3
24. García‐Jacas, C. R., Marrero‐Ponce, Y., Acevedo‐Martínez, L., Barigye, S. J., Valdés‐Martiní, J. R., & Contreras‐Torres, E. (2014). QuBiLS-MIDAS: A parallel free-software for molecular descriptors computation based on multilinear algebraic maps. Journal of Computational Chemistry, 35(18), 1395–1409. https://doi.org/10.1002/jcc.23640
25. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter, 11(1), 10–18. https://doi.org/10.1145/1656274.1656278
26. Thirumalai, K., Singh, A., & Ramesh, R. (2011). A MATLABTM code to perform weighted linear regression with (correlated or uncorrelated) errors in bivariate data. Journal of the Geological Society of India, 77(4), 377–380. https://doi.org/10.1007/s12594-011-0044-1
27. Seeger, M. (2004). Gaussian processes for machine learning. International Journal of Neural Systems, 14(02), 69–106. https://doi.org/10.1142/S0129065704001899
28. Cabrera, N., Mora, J. R., & Marquez, E. A. (2019). Computational Molecular Modeling of Pin1 Inhibition Activity of Quinazoline, Benzophenone, and Pyrimidine Derivatives. Journal of Chemistry, 2019, 1–11. https://doi.org/10.1155/2019/2954250
29. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
30. Li, C., & Jiang, L. (2006). Using Locally Weighted Learning to Improve SMOreg for Regression. En Q. Yang & G. Webb (Eds.), PRICAI 2006: Trends in Artificial Intelligence (pp. 375–384). Springer. https://doi.org/10.1007/978-3-540-36668-3_41
31. Veerasamy, R., Rajak, H., Jain, A., Sivadasan, S., Varghese, C. P., & Agrawal, R. K. (2011). Validation of QSAR Models—Strategies and Importance. International Journal of Drug Design and Discovery, 2(3), 511–519.
32. Gramatica, P., Chirico, N., Papa, E., Cassani, S., & Kovarich, S. (2013). QSARINS: A new software for the development, analysis, and validation of QSAR MLR models. Journal of Computational Chemistry, 34(24), 2121–2132. https://doi.org/10.1002/jcc.23361
33. Cabrera, N., Mora, J. R., Márquez, E., Flores-Morales, V., Calle, L., & Cortés, E. (2021). QSAR and molecular docking modelling of anti-leishmanial activities of organic selenium and tellurium compounds. SAR and QSAR in Environmental Research, 32(1), 29–50. https://doi.org/10.1080/1062936X.2020.1848914
34. Montgomery, D. C., & Runger, G. C. (2014). Applied Statistics and Probability for Engineers (6a ed.). John Wiley & Sons.
35. Mao, J. X. (2014). Atomic Charges in Molecules: A Classical Concept in Modern Computational Chemistry. Journal of Postdoctoral Research, 2(2), 4. https://doi.org/10.14304/SURYA.JPR.V2N2.2
36. Gupta, V. P. (2016). 12—Characterization of Chemical Reactions. En V. P. Gupta (Ed.), Principles and Applications of Quantum Chemistry (pp. 385–433). Academic Press. https://doi.org/10.1016/B978-0-12-803478-1.00012-1
37. House, J. E. (2013). Chapter 9—Acid–Base Chemistry. En J. E. House (Ed.), Inorganic Chemistry (Second Edition) (pp. 273–312). Academic Press. https://doi.org/10.1016/B978-0-12-385110-9.00009-1