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Molecular descriptor from atomic weighted vectors to predict aquatic toxicity
Juan Castillo-Garit 1 , Yoan Martínez-López 2 , Stephen Barigye 3 , Oscar Martínez-Santiago 4 , Yovani Marrero-Ponce 5 , James Green 6

1  Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Department of Pharmacy, Faculty of Chemical-Pharmacy. Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba,Bioinformatic Research in Systems & Computer Engineering, Carleton University, Ottawa, Canada
2  Department of Computer Sciences, Faculty of Informatics, Camaguey University, Camaguey City, 74650, Camaguey Cuba
3  Departamento de Química, Universidade Federal de Lavras, CP 3037, 37200-000, Lavras, MG, Brazil
4  Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Department of Pharmacy, Faculty of Chemical-Pharmacy. Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba
5  Grupo de Investigación en Estudios Químicos y Biológicos, Facultad de Ciencias Básicas, Universidad Tecnológica de Bolívar, Cartagena de Indias, Bolívar, Colombia
6  Bioinformatic Research in Systems & Computer Engineering, Carleton University, Ottawa, Canada

Published: 16 January 2017 by MDPI AG in Proceedings of MOL2NET 2016, International Conference on Multidisciplinary Sciences, 2nd edition in MOL2NET 2016, International Conference on Multidisciplinary Sciences, 2nd edition
MDPI AG, 10.3390/mol2net-02-03865
Abstract:

Molecular descriptors from atom weighted vectors (MD-AWV) are used in the prediction of aquatic toxicity of a large group of organic compounds of 392 benzene derivatives to the protozoo ciliate Tetrahymena pyriformis (log (IGC50) −1). These descriptors are calculated using the MD-LOVIs software and various Aggregation Operators are examined with the aim comparing their performances in predicting aquatic toxicity. Variability analysis is used to quantify the information content of these molecular descriptors by means of an information theory-based algorithm. Principal Component Analysis (PCA) is used to analyze the orthogonality of these descriptors and it is observed that MD-AWV provide linearly independent information from that of descriptors generated using the popular DRAGON package (0-2D). Multiple Linear Regression with Genetic Algorithms is used to obtain models of the structure–toxicity relationships; the best model shows values of Q2= 0.830 and R2=0.837 using six variables. Our models compare favorably with other previously published models that use the same data set. The obtained results suggest that MD-AWV provide an effective alternative for determining aquatic toxicity of benzene derivatives.


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