The inclusion and accessibility of different methodologies to explore chemical datasets has been beneficial to the field of Quantitative Structure-Activity Relationship (QSAR) modeling. Several molecular systems that historically do not perform well using traditional and three-dimensional QSAR methodologies have been adequately explained using contemporary QSAR modeling methods and protocols. The ability of these methodologies and protocols to accommodate large datasets (several thousands compounds) that are chemically diverse � and in the case of classification modeling unbalanced (one experimental outcome dominates the dataset) � allows scientists to further explore a remarkable amount of biological and chemical information. In this study, we explore, analyze, and discuss the creation of a continuous Tetrahymena pyriformis (T. pyriformis) model using Support Vector Regression (SVR) techniques. The models are constructed with three types of molecular descriptors that capture the gross physicochemical features of the compounds: (i) 2D, 2 �D, and 3D physical features, (ii) VolSurf-like molecular interaction fields, and (iii) 4D-Fingerprints. The best model had an R2 value of 0.924 for the training set and was able to predict the continuous endpoints for two test sets with R2 values of 0.832 and 0.620. The studies presented within demonstrate the predictive ability (classification and continuous endpoints) of QSAR models constructed from curated datasets, biologically relevant molecular descriptors, and Support Vector Regression.

Thriving with contemporary QSAR methodologies: Exploring endpoint prediction of <i>Tetrahymena pyriformis</i> aqueous toxicity

243rd National Meeting (2012)

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Thriving with contemporary QSAR methodologies: Exploring endpoint prediction of Tetrahymena pyriformis aqueous toxicity