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Iurii Sushko
Researcher at University of British Columbia
Publications - 17
Citations - 1399
Iurii Sushko is an academic researcher from University of British Columbia. The author has contributed to research in topics: Applicability domain & Quantitative structure–activity relationship. The author has an hindex of 9, co-authored 17 publications receiving 1146 citations. Previous affiliations of Iurii Sushko include University of North Carolina at Chapel Hill.
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Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information
Iurii Sushko,Sergii Novotarskyi,Robert Körner,Anil Kumar Pandey,Matthias Rupp,Wolfram Teetz,Stefan Brandmaier,Ahmed Abdelaziz,Volodymyr V. Prokopenko,Vsevolod Yu. Tanchuk,Roberto Todeschini,Alexandre Varnek,Gilles Marcou,Peter Ertl,Vladimir Potemkin,Maria Grishina,Johann Gasteiger,Christof H. Schwab,Igor I. Baskin,Vladimir A. Palyulin,Eugene V. Radchenko,William J. Welsh,Vladyslav Kholodovych,Dmitriy Chekmarev,Artem Cherkasov,João Aires-de-Sousa,Qingyou Zhang,Andreas Bender,Florian Nigsch,Luc Patiny,Antony J. Williams,Valery Tkachenko,Igor V. Tetko +32 more
TL;DR: The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling and to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community.
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Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection
Igor V. Tetko,Iurii Sushko,Anil Kumar Pandey,Hao Zhu,Alexander Tropsha,Ester Papa,Tomas Öberg,Roberto Todeschini,Denis Fourches,Alexandre Varnek +9 more
TL;DR: It is shown that incorrect validation of a model may result in the wrong estimation of its performance and suggested how this problem could be circumvented and the distance to model metric could also be used to augment mechanistic QSAR models by estimating their prediction errors.
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Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set.
Iurii Sushko,Sergii Novotarskyi,Robert Körner,Anil Kumar Pandey,Artem Cherkasov,Jiazhong Li,Paola Gramatica,Katja Hansen,Timon Schroeter,Klaus-Robert Müller,Lili Xi,Huanxiang Liu,Xiaojun Yao,Tomas Öberg,Farhad Hormozdiari,Phuong Dao,Cenk Sahinalp,Roberto Todeschini,Pavel G. Polishchuk,A. Artemenko,Victor E. Kuz’min,Todd M. Martin,Douglas M. Young,Denis Fourches,Eugene N. Muratov,Alexander Tropsha,Igor I. Baskin,Dragos Horvath,Gilles Marcou,Christophe Muller,A. Varnek,Volodymyr V. Prokopenko,Igor V. Tetko +32 more
TL;DR: This work demonstrates that the DMs based on an ensemble (consensus) model provide systematically better performance than other DMs and can be used to halve the cost of experimental measurements by providing a similar prediction accuracy.
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ToxAlerts: a Web server of structural alerts for toxic chemicals and compounds with potential adverse reactions.
TL;DR: A Web-based platform for collecting and storing toxicological structural alerts from literature and for virtual screening of chemical libraries to flag potentially toxic chemicals and compounds that can cause adverse side effects is presented.
Journal ArticleDOI
Applicability domain for in silico models to achieve accuracy of experimental measurements
Iurii Sushko,Sergii Novotarskyi,Robert Körner,Anil Kumar Pandey,Vasily V. Kovalishyn,Volodymyr V. Prokopenko,Igor V. Tetko +6 more
TL;DR: It is shown that applicability domain (AD) approaches can differentiate reliable and non‐reliable predictions and identify those with experimental accuracy for both regression and classification models.