Natalia Kireeva
Natalia Kireeva
Senior Scientific Researcher, Frumkin Institute of Physical Chemistry and Electrochemistry RAS
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Exhaustive QSPR studies of a large diverse set of ionic liquids: how accurately can we predict melting points?
A Varnek, N Kireeva, IV Tetko, II Baskin, VP Solov'ev
Journal of chemical information and modeling 47 (3), 1111-1122, 2007
Generative topographic mapping (GTM): universal tool for data visualization, structure‐activity modeling and dataset comparison
N Kireeva, II Baskin, HA Gaspar, D Horvath, G Marcou, A Varnek
Molecular informatics 31 (3‐4), 301-312, 2012
The one‐class classification approach to data description and to models applicability domain
II Baskin, N Kireeva, A Varnek
Molecular Informatics 29 (8‐9), 581-587, 2010
Materials space of solid-state electrolytes: Unraveling chemical composition–structure–ionic conductivity relationships in garnet-type metal oxides using cheminformatics …
N Kireeva, VS Pervov
Physical Chemistry Chemical Physics 19 (31), 20904-20918, 2017
Toward Navigating Chemical Space of Ionic Liquids: Prediction of Melting Points Using Generative Topographic Maps
N Kireeva, SL Kuznetsov, AY Tsivadze
Industrial & Engineering Chemistry Research 51, 14337−14343, 2012
Structure-property modelling of complex formation of strontium with organic ligands in water
VP Solov’ev, NV Kireeva, AY Tsivadze, AA Varnek
Journal of Structural Chemistry 47 (2), 298-311, 2006
QSPR ensemble modelling of alkaline-earth metal complexation
VP Solov’ev, N Kireeva, AY Tsivadze, A Varnek
Journal of Inclusion Phenomena and Macrocyclic Chemistry 76 (1-2), 159-171, 2013
Using Self-Organizing maps to accelerate similarity search
F Bonachera, G Marcou, N Kireeva, A Varnek, D Horvath
Bioorganic & Medicinal Chemistry, 2012
Materials Informatics Screening of Li‐Rich Layered Oxide Cathode Materials with Enhanced Characteristics Using Synthesis Data
N Kireeva, VS Pervov
Batteries & Supercaps 3, 427, 2020
The complexation of metal ions with various organic ligands in water: Prediction of stability constants by QSPR ensemble modelling
V Solov’ev, N Kireeva, S Ovchinnikova, A Tsivadze
Journal of Inclusion Phenomena and Macrocyclic Chemistry 83, 89-101, 2015
Computer-aided design of new metal binders
A Varnek, D Fourches, N Kireeva, O Klimchuk, G Marcou, A Tsivadze, ...
Radiochimica Acta 96 (8), 505-511, 2008
Towards in silico identification of the human ether-a-go-go-related gene channel blockers: discriminative vs. generative classification models
N Kireeva, SL Kuznetsov, AA Bykov, AY Tsivadze
SAR and QSAR in Environmental Research, 1-15, 2013
Impact of distance-based metric learning on classification and visualization model performance and structure–activity landscapes
NV Kireeva, SI Ovchinnikova, SL Kuznetsov, AM Kazennov, AY Tsivadze
Journal of computer-aided molecular design 28, 61-73, 2014
Machine Learning Analysis of Microwave Dielectric Properties for Seven Structure Types: The Role of the Processing and Composition
N Kireeva, VP Solov’ev
Journal of Physics and Chemistry of Solids, 110178, 2021
Nonlinear Dimensionality Reduction for Visualizing Toxicity Data: Distance‐Based Versus Topology‐Based Approaches
NV Kireeva, SI Ovchinnikova, IV Tetko, AM Asiri, KV Balakin, AY Tsivadze
ChemMedChem 9 (5), 1047-1059, 2014
Supervised extensions of chemography approaches: case studies of chemical liabilities assessment
SI Ovchinnikova, AA Bykov, AY Tsivadze, EP Dyachkov, NV Kireeva
Journal of Cheminformatics 6, 1-18, 2014
New possibilities to obtain ceramic nanoheterostructures with enhanced ionic conductivity
VS Pervov, EV Makhonina, AE Zotova, NV Kireeva, IMA Kedrinsky
Nanotechnologies in Russia 9, 347-355, 2014
Modeling ionic conductivity and activation energy in garnet-structured solid electrolytes: The role of composition, grain boundaries and processing
NV Kireeva, AY Tsivadze, VS Pervov
Solid State Ionics 399 (15), 116293, 2023
Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine Learning
NV Kireeva, AY Tsivadze, VS Pervov
Batteries 9, 430, 2023
A Machine Learning-Based Study of Li+ and Na+ Metal Complexation with Phosphoryl-Containing Ligands for the Selective Extraction of Li+ from Brine
N Kireeva, VE Baulin, AY Tsivadze
ChemEngineering 7 (3), 41, 2023
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