| 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 | 179 | 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 | 158 | 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 | 64 | 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 | 46 | 2017 |
| Using Self-Organizing maps to accelerate similarity search F Bonachera, G Marcou, N Kireeva, A Varnek, D Horvath Bioorganic & Medicinal Chemistry, 2012 | 27 | 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 | 27 | 2006 |
| 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 | 26 | 2012 |
| 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 | 25 | 2013 |
| 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 | 24 | 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 | 21 | 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 | 19 | 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 | 16 | 2013 |
| 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 | 13 | 2021 |
| 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 | 12 | 2014 |
| 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 | 11 | 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 | 7 | 2023 |
| 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 | 6 | 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 | 6 | 2014 |
| Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine Learning NV Kireeva, AY Tsivadze, VS Pervov Batteries 9, 430, 2023 | 5 | 2023 |
| Machine learning-based evaluation of functional characteristics of Li-rich layered oxide cathode materials using the data of XPS and XRD spectra NV Kireeva, VS Pervov, AY Tsivadze Computational Materials Science 231, 112591, 2023 | 4 | 2023 |
Alexandre VarnekProfessor of Chemistry, University of StrasbourgVerified email at unistra.fr
