V
Vsevolod Yu. Tanchuk
Researcher at National Academy of Sciences of Ukraine
Publications - 34
Citations - 3489
Vsevolod Yu. Tanchuk is an academic researcher from National Academy of Sciences of Ukraine. The author has contributed to research in topics: Protein tyrosine phosphatase & Docking (molecular). The author has an hindex of 14, co-authored 34 publications receiving 3081 citations. Previous affiliations of Vsevolod Yu. Tanchuk include University of Lausanne.
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Journal ArticleDOI
Virtual computational chemistry laboratory - design and description
Igor V. Tetko,Johann Gasteiger,Roberto Todeschini,Andrea Mauri,David J. Livingstone,Peter Ertl,Vladimir A. Palyulin,Eugene V. Radchenko,Nikolai S. Zefirov,Alexander S. Makarenko,Vsevolod Yu. Tanchuk,Volodymyr V. Prokopenko +11 more
TL;DR: The main features and statistics of the developed system, Virtual Computational Chemistry Laboratory, allowing the computational chemist to perform a comprehensive series of molecular indices/properties calculations and data analysis are reviewed.
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Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program.
TL;DR: This article provides a systematic study of several important parameters of the Associative Neural Network, such as the number of networks in the ensemble, distance measures, neighbor functions, selection of smoothing parameters, and strategies for the user-training feature of the algorithm.
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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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Prediction of n-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E-state indices.
TL;DR: It is shown that the diversity of the training sets rather than the design of the methods is the main factor determining their prediction ability for new data, and the ALOGPS method provided better prediction ability than the other tested methods.
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Estimation of aqueous solubility of chemical compounds using E-state indices.
TL;DR: Smaller neural networks and use of one homogeneous set of parameters provides a more robust model for prediction of aqueous solubility of chemical compounds.