Vol. 13 No. 2 (2021), SECTION C: ENGINEERING
Vol. 13 No. 2 (2021)

Quantitative structure activity relationship of bioconcentration factor of polychlorinated biphenyls in fish species using machine learning: Relación cuantitativa estructura actividad del factor de bioconcentración de los bifenilos policlorados

SECTION C: ENGINEERING
Published 2021-11-16
Martín Moreno
Universidad San Francisco de Quito USFQ
https://orcid.org/0000-0001-8189-148X
José Ramón Mora
USFQ
https://orcid.org/0000-0001-6128-9504
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Keywords

Linear regression
PCBs
molecular descriptors
molecular mechanics
marine species

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Moreno, M., & Mora, J. R. (2021). Quantitative structure activity relationship of bioconcentration factor of polychlorinated biphenyls in fish species using machine learning: 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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Abstract

Polychlorinated biphenyls (PCBs) are persistent pollutants that greatly affect marine ecosystems. Machine learning techniques were used to build quantitative structure activity-relationship (QSAR) models that predict PCBs"™ bioconcentration factor (BCF). These models were built from topographic 2D and 3D descriptors calculated for the molecular structures optimized at molecular mechanics level of theory. After the analysis of their statistical parameters, it was determined that two models are robust enough for predicting logBCF. The models selected were: M_4_LR, built with two molecular descriptors and showed values of r2 = 0,9154, Q2LOO = 0,8944, y Q2ext = 0,9119, and M_13, built with four molecular descriptors and showed values of r2 = 0,9375, Q2LOO = 0,9155, y Q2ext = 0,844. Both models passed the double validation phase, and they satisfied the criteria from the Tropsha"™s test. This implies that predictions for logBCF were quite accurate as it is showed in the results from the present study.

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References

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