Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Set

Sushko, I.; Novotarskyi, S.; Körner, R.; Pandey, A.K.; Cherkasov, A.; Jiazhong, L.; Gramatica, P.; Hansen, K.; Schroeter, T.; Müller, K.; Lili, X.; Liu, H.; Yao, X.; Öberg, T.; Hormozdiari, F.; Dao, P.; Sahinalp, C.; Todeschini, R.; Polishchuk, P.; Artemenko, A.; Kuz’min, V.; Martin, T.M.; Young, D.M.; Fourches, D.; Muratov, E.; Tropsha, A.; Baskin, I.; Horvath, D.; Marcou, G.; Muller, C.; Varnek, A.; Prokopenko, V.V.; Tetko, I.V.

Авторы: Sushko Iurii, Novotarskyi Sergii, Körner Robert, Pandey Anil Kumar, Cherkasov Artem, Jiazhong Li, Gramatica Paola, Hansen Katja, Schroeter Timon, Müller Klaus-Robert, Lili Xi, Liu Huanxiang, Yao Xiaojun, Öberg Tomas, Hormozdiari Farhad, Dao Phuong, Sahinalp Cenk, Todeschini Roberto, Polishchuk Pavel, Artemenko Anatoliy, Kuz’min Victor, Martin Todd M., Young Douglas M., Fourches Denis, Muratov Eugene, Tropsha Alexander, Baskin Igor, Horvath Dragos, Marcou Gilles, Muller Christophe, Varnek Alexander, Prokopenko Volodymyr V., Tetko Igor V.
Журнал: Journal of Chemical Information and Modeling
Том: 50
Номер: 12
Год издания: 2010
Издательство: American Chemical Society
Местоположение издательства: United States
Первая страница: 2094
Последняя страница: 2111
DOI: 10.1021/ci100253r
Аннотация: The estimation of accuracy and applicability of QSAR and QSPR models for biological and physicochemical properties represents a critical problem. The developed parameter of “distance to model” (DM) is defined as a metric of similarity between the training and test set compounds that have been subjected to QSAR/QSPR modeling. In our previous work, we demonstrated the utility and optimal performance of DM metrics that have been based on the standard deviation within an ensemble of QSAR models. The current study applies such analysis to 30 QSAR models for the Ames mutagenicity data set that were previously reported within the 2009 QSAR challenge. We demonstrate that the DMs based on an ensemble (consensus) model provide systematically better performance than other DMs. The presented approach identifies 30−60% of compounds having an accuracy of prediction similar to the interlaboratory accuracy of the Ames test, which is estimated to be 90%. Thus, the in silico predictions can be used to halve the cost of experimental measurements by providing a similar prediction accuracy. The developed model has been made publicly available at http://ochem.eu/models/1.
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Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Setстатья