V
Vladimir Poroikov
Researcher at Institute of Business & Medical Careers
Publications - 256
Citations - 7699
Vladimir Poroikov is an academic researcher from Institute of Business & Medical Careers. The author has contributed to research in topics: Medicine & Chemistry. The author has an hindex of 39, co-authored 231 publications receiving 5835 citations. Previous affiliations of Vladimir Poroikov include Russian Academy of Sciences & Norwegian University of Science and Technology.
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Journal ArticleDOI
PASS: prediction of activity spectra for biologically active substances
TL;DR: A WWW server for the on-line prediction of the biological activity spectra of substances has been constructed and a WWW interface for the PASS software is developed.
Journal ArticleDOI
Prediction of the Biological Activity Spectra of Organic Compounds Using the Pass Online Web Resource
Dmitry Filimonov,Alexey Lagunin,Tatyana A. Gloriozova,A. V. Rudik,D. S. Druzhilovskii,Pavel V. Pogodin,Pavel V. Pogodin,Vladimir Poroikov,Vladimir Poroikov +8 more
TL;DR: In this paper, the authors present a web resource for the prediction of the biological activity spectra of organic compounds based on their structural formulas for more than 4000 types of biological activity with average accuracy above 95% (http://www.way2drug.com/passonline ).
Journal ArticleDOI
QSAR without borders
Eugene N. Muratov,Eugene N. Muratov,Jürgen Bajorath,Robert P. Sheridan,Igor V. Tetko,Dmitry Filimonov,Vladimir Poroikov,Tudor I. Oprea,Tudor I. Oprea,Tudor I. Oprea,Igor I. Baskin,Igor I. Baskin,Alexandre Varnek,Adrian E. Roitberg,Olexandr Isayev,Stefano Curtalolo,Denis Fourches,Yoram Cohen,Alán Aspuru-Guzik,David A. Winkler,Dimitris K. Agrafiotis,Artem Cherkasov,Alexander Tropsha +22 more
TL;DR: This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed inQSAR to a wide range of research areas outside of traditional QSar boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics.
Journal ArticleDOI
QSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction
TL;DR: Comparison of accuracy for QSAR models obtained separately using QNA descriptors, PASS predictions, nearest neighbours’ assessment with consensus models clearly demonstrated the benefits of consensus prediction.
Journal ArticleDOI
Robustness of biological activity spectra predicting by computer program PASS for noncongeneric sets of chemical compounds.
TL;DR: It is demonstrated that predictions are robust despite the exclusion of up to 60% of information and the influence on the accuracy of predicting the types of activity with PASS by a reduction of the number of structures in the training set and a number of known activities in theTraining set.