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ACI Avances en Ciencias e Ingenierías

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

DOI
https://doi.org/10.18272/aci.v13i2.2275
Submitted
May 11, 2021
Published
2021-11-16

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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