Molecular Descriptor from Atomic Weighted Vectors to Predict Aquatic Toxicity

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 MDAWV 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.