Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis

Abstract

Selecting most rigorous quantitative structure-activity relationship (QSAR) approaches is of great importance in the development of robust and predictive models of chemical toxicity. To address this issue in a systematic way, we have formed an international virtual collaboratory consisting of six independent groups with shared interests in computational chemical toxicology. We have compiled an aqueous toxicity data set containing 983 unique compounds tested in the same laboratory over a decade against Tetrahymena pyriformis. A modeling set including 644 compounds was selected randomly from the original set and distributed to all groups that used their own QSAR tools for model development. The remaining 339 compounds in the original set (external set I) as well as 110 additional compounds (external set II) published recently by the same laboratory (after this computational study was already in progress) were used as two independent validation sets to assess the external predictive power of individual models. In total, our virtual collaboratory has developed 15 different types of QSAR models of aquatic toxicity for the training set. The internal prediction accuracy for the modeling set ranged from 0.76 to 0.93 as measured by the leave-one-out cross-validation correlation coefficient ( Q abs2). The prediction accuracy for the external validation sets I and II ranged from 0.71 to 0.85 (linear regression coefficient R absI2) and from 0.38 to 0.83 (linear regression coefficient R absII2), respectively. The use of an applicability domain threshold implemented in most models generally improved the external prediction accuracy but at the same time led to a decrease in chemical space coverage. Finally, several consensus models were developed by averaging the predicted aquatic toxicity for every compound using all 15 models, with or without taking into account their respective applicability domains. We find that consensus models afford higher prediction accuracy for the external validation data sets with the highest space coverage as compared to individual constituent models. Our studies prove the power of a collaborative and consensual approach to QSAR model development. The best validated models of aquatic toxicity developed by our collaboratory (both individual and consensus) can be used as reliable computational predictors of aquatic toxicity and are available from any of the participating laboratories.

Consensus QSAR Modeling of Phosphor‐Containing Chiral AChE Inhibitors

“…Such approach is believed to yield more accurate results [26]. The difference between the current approach and a classical consensus modeling [48] is caused by the absence of confidence that all the models obtained using different descriptors are suitable for QSAR modeling of the mixtures of chiral isomers. Another important factor is their unwillingness to use a fortiori pointless models.…”

The potential to use QSAR to populate ecotoxicity characterisation factors for simplified LCIA and chemical prioritisation

“…The endpoints estimated were fathead minnow LC 50 96 h and Daphnia magna LC 50 48 h . They were estimated by the consensus model in TEST as it has been shown that consensus models give a better estimate of the toxicity and also covers a larger applicable domain than the individual models (Zhu et al 2008). The consensus model is the average value from the predicted values for several different methods.…”

Quantitative Structure-Activity Relation Study of Quaternary Ammonium Compounds in Pathogen Control: Computational Methods for the Discovery of Food Antimicrobials

“…Many studies have pointed to the effectiveness of using a consensus model for increasing the accuracy of the prediction of unknown models, rather than using a single model [36,37]. Using the twelve previously identified models, we averaged the predictions on the validation set to develop three different consensus models (Table 2 and Figure 3).…”