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Cited by 287

The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.

Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis

Zhu, Hao, Tropsha, Alexander, Fourches, Denis, Varnek, Alexandre, Papa, Ester, Gramatica, Paola, Öberg, Tomas, Dao, Phuong, Cherkasov, Artem, Tetko, Igor V.·2008·Journal of Chemical Information and Modeling
DOI 10.1021/ci700443v
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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.

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Metadata

Authors
Zhu, H., Tropsha, A., Fourches, D., Varnek, A., Papa, E., Gramatica, P., Öberg, T., Dao, P., Cherkasov, A., & Tetko, I. V.
Venue
Journal of Chemical Information and Modeling
Publisher
American Chemical Society (ACS)
Published
2008-03-01
Volume
48
Issue
4
Pages
766-784
ISSN
  • 1549-9596 (ISSN-L)
  • 1549-960X
DOI
10.1021/ci700443v
OpenAlex
W2157851318
Cited by
287
References
57
FWCI
27.87
Type
journal article
Language
en
Open access
closed

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