A. Academic Background
Igor Baskin academic background
2010 | Habilitation in computational chemistry | Lomonosov Moscow State University, Russia | Modeling properties of chemical compounds using artificial neural networks and fragment descriptors |
1990 | PhD in organic chemistry | Lomonosov Moscow State University, Russia | Computer-assisted systematic generation and analysis of organic reactions |
1984 | MSc in chemistry | Lomonosov Moscow State University, Russia | Theoretical conformational analysis |
B. Previous Employment
Igor Baskin previous employment
2021-present | Technion – Israel Institute of Technology (Haifa, Israel) | Senior researcher | AI and data science in electrochemistry, first principles modeling and atomistic simulations |
2013-2020 | Faculty of Physics, Lomonosov Moscow State University (Russia) | Leading researcher | Chemoinformatics and materials informatics |
2015-2020 | Butlerov Institute of Chemistry, Kazan Federal University, Russia (part-time) | Senior researcher | Chemoinformatics, machine learning |
2005-2020 | Faculty of Chemistry, Strasbourg University, France (multiple times) | Visiting scientist, invited professor | Chemoinformatics, machine learning |
2001-2013 | Department of Chemistry, Lomonosov Moscow State University, Russia | Senior and leading researcher | Molecular modeling, medicinal chemistry, chemoinformatics |
1994-2001 | Zelinsky Institute of Organic Chemistry, Moscow, Russia | Senior researcher | Computational chemistry, neural networks |
1988-1994 | Semenov Institute of Chemical Physics, Moscow, USSR and Russia | Research engineer and researcher | Quantum chemistry and atomistic simulations, photochromic dyes, mathematical chemistry, chemical databases, QSAR |
C. Research Experience
- Chemoinformatics and materials informatics
- Artificial intelligence, machine learning (neural networks and deep learning, kernel-based and Bayesian methods, etc) and data science in chemistry and materials science
- Relationships between structures and properties of chemical compounds (QSAR/QSPR)
- Computational and medicinal chemistry, molecular modeling of supramolecular complexes, proteins and protein-ligand interactions, docking, molecular dynamics, virtual screening, de novo molecular design
- Mathematical chemistry, chemical graph theory
- Informatics of chemical reactions
D. Publications
226 scientific publications, including: 6 books, 18 book chapters, 18 invited review articles, 158 research papers in peer-reviewed journals, 22 conference proceedings, 4 deposited research reports and other publications.
3778 citations, H-index is 30 (Web of Science); 6700 citations, H-index is 40 (Google Scholar).
E. Top Cited Publications
- A. Cherkasov, E.N. Muratov, D. Fourches, et al., QSAR modeling: Where have you been? where are you going to? Journal of Medicinal Chemistry, 57(12), 4977-5010 (2014)
- I. Sushko, S. Novotarskyi, R. Koerner, et al., Online chemical modeling environment (OCHEM): Web platform for data storage, model development and publishing of chemical information. Journal of Computer-Aided Molecular Design, 25(6), 533-554 (2011)
- E.N. Muratov, J. Bajorath, R.P. Sheridan, et al. QSAR without borders. Chemical Society Reviews, 49(11), 3525-3564 (2020)
- I. Sushko, S. Novotarskyi, R. Körner, et al., Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set. Journal of Chemical Information and Modeling, 50(12), 2094-2111 (2010)
- A. Varnek, I. Baskin. Machine Learning Methods for Property Prediction in Chemoinformatics: Quo Vadis? Journal of Chemical Information and Modeling, 52(6), 1413-1437 (2012)
- I.I. Baskin, D. Winkler, I.V. Tetko. A renaissance of neural networks in drug discovery. Expert Opinion on Drug Discovery, 11(8), 785-795 (2016)
- A. Varnek, N. Kireeva, I.V. Tetko, et al. Exhaustive QSPR Studies of Large Diverse Set of Ionic Liquids: How Accurately Can We Predict Melting Points? Journal of Chemical Information and Modeling, 47(3), 1111-1122 (2007)
- M.I. Skvortsova, I.I. Baskin, O.I. Slovokhotova, et al. Inverse Problem in QSAR/QSPR Studies for the Case of Topological Indices Characterizing Molecular Shape (Kier Indices). Journal of Chemical Informatics and Computer Sciences, 33(4), 630-634 (1993)
- N. Kireeva, I.I. Baskin, H.A. Gaspar, et al. Generative Topographic Mapping (GTM): Universal Tool for Data Visualization, Structure-Activity Modeling and Dataset Comparison. Molecular Informatics, 31(3-4), 301-312 (2012)
- A. Varnek, I. Baskin. Chemoinformatics as a Theoretical Chemistry Discipline. Molecular Informatics, 30(1), 20-32 (2011)
- H.A. Gaspar, I.I. Baskin, G. Marcou, G., et al. Chemical Data Visualization and Analysis with Incremental GTM: Big Data Challenge. Journal of Chemical Information and Modeling, 55(1), 84-94 (2015)
- A. Varnek, C. Gaudin, G. Marcou, et al. Inductive Transfer of Knowledge: Application of Multi-Task Learning and Feature Net Approaches to Model Tissue-Air Partition Coefficients. Journal of Chemical Information and Modeling, 49(1), 133-144 (2009)
- I.I. Baskin, V.A. Palyulin, N.S. Zefirov. A Neural Device for Searching Direct Correlations between Structures and Properties of Organic Compounds. Journal of Chemical Informatics and Computer Sciences, 37(4), 715-721 (1997)
- I.I. Baskin, M.I. Skvortsova, I.V. Stankevich, et al. On Basis of Invariants of Labeled Molecular Graphs. Journal of Chemical Informatics and Computer Sciences, 35(3), 527-531 (1995)
- B. Sattarov, I.I. Baskin, D. Horvath, et al. De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping. Journal of Chemical Information and Modeling, 59(3), 1182-1196 (2019)
- N.M. Halberstam, I.I.Baskin, V.A. Palyulin, et al. Neural Networks as a Method of Revealing Structure-Property Relationships for Organic Compounds. Russian Chemical Reviews, 72(7), 629-649 (2003)
- I.I. Baskin, V.A. Palyulin, N.S. Zefirov. Neural Networks in Building QSAR Models. In: Artificial Neural Networks: Methods and Protocols (Livingstone D.S., ed.), Humana Press, a part of Springer Science + Business Media, 458, 139-160 (2008)
- S. Bachurin, S. Tkachenko, I. Baskin, et al. Neuroprotective and Cognition Enhancing Properties of MK-801 Flexible Analogs. Structure-Activity Relationships. The Annals of the New York Academy of Sciencies, 931, 219-236 (2001)
- S.P. Gromov, E.N. Ushakov, O.A. Fedorova, et al. Novel Photoswitchable Receptors: Synthesis and Cation-Induced Self-Assembly into Dimeric Complexes Leading to Stereospecific [2+2]-Photocycloaddition of Styryl Dyes Containing a 15-Crown-5 Ether Unit. Journal of Organic Chemistry, 68(16), 6115-6125 (2003)
- I.I. Baskin, A.O. Ait, N.M. Halberstam, et al. An Approach to the Interpretation of Backpropagation Neural Network Models in QSAR studies. SAR and QSAR in Environmental Research, 13(1), pp. 35-41 (2002)
- I.I. Baskin, A. Varnek. Fragment Descriptors in SAR/QSAR/QSPR Studies, Molecular Similarity Analysis and in Virtual Screening. In: Chemoinformatics Approaches to Virtual Screening (Varnek A., Tropsha A., eds.), RCS Publishing, pp. 1-43 (2008)
- H.A. Gaspar, I.I. Baskin, G. Marcou, et al. GTM-Based QSAR Models and Their Applicability Domains. Molecular Informatics, 34(6-7), 348-356 (2015)
- N.I. Zhokhova, I.I. Baskin, V.A. Palyulin, et al. Fragmental Descriptors with Labeled Atoms and Their Application in QSAR/QSPR Studies. Doklady Chemistry, 417, 282-284 (2007)
- N.V. Artemenko, I.I. Baskin, V.A. Palyulin, et al. Prediction of Physical Properties of Organic Compounds Using Artificial Neural Networks within the Substructure Approach. Doklady Chemistry, 381, 317-320 (2001)
- I. Baskin, N. Kireeva, A. Varnek. The One-Class Classification Approach to Data Description and to Models Applicability Domain. Molecular Informatics, 29(8-9), 581-587 (2010)
- S.P. Gromov, O.A. Fedorova, E.N. Ushakov, et al. Photoswitchable molecular pincers: synthesis, self-assembly into sandwich complexes and ion-selective intramolecular [2+2]-photocycloaddition of an unsaturated bis-15-crown-5 ether. Journal of Chemistry Society, Perkin Trans. 2, 1323-1330 (1999)
- N.V. Artemenko, I.I. Baskin, V.A. Palyulin, et al. Artificial Neural Networks and Fragmental Approach in Predicting Physico-Chemical Properties of Organic Compounds. Russian Chemistry Bulletin, 52(1), 20-29 (2003)
- E.N. Ushakov, S.P. Gromov, A.V. Buevich, et al. Crown-containing styryl dyes: cation-induced self-assembly of multiphotochromic 15-crown-5 ethers into photoswitchable molecular devices. Journal of Chemistry Society, Perkin Trans. 2, 601-607 (1999)
- I.I. Baskin, T.I. Madzhidov, I.S. Antipin, et al. Artificial Intelligence in Synthetic Chemistry: Achievements and Perspectives. Russian Chemical Reviews, 86(11), 1127-1156 (2017)
- T. Madzhidov, P. Polishchuk, R. Nugmanov, et al. Structure-reactivity relationships in terms of the condensed graphs of reactions. Russian Journal of Organic Chemistry, 50(4), 459-463 (2014)
- V. Chupakhin, G. Marcou, I. Baskin, et al. Predicting Ligand Binding Modes from Neural Networks Trained on Protein-Ligand Interaction Fingerprints. Journal of Chemical Information and Modeling, 53(4), 763-772 (2013)
- I. Baskin, A. Varnek. Building a chemical space based on fragment descriptors. Comb. Chem. High Throughput Screening, 11(8), 661-668 (2008)
- O.E. Skirgello, I.V. Balyasnikova, P.V. Binevski, et al. Inhibitory antibodies to human Angiotensin-converting enzyme: fine epitope mapping and mechanism of action. Biochemistry, 45(15), 4831-4847 (2006)
- G.F. Makhaeva, E.V. Radchenko, I.I. Baskin, et al. Combined QSAR studies of inhibitor properties of O-phosphorylated oximes toward serine esterases involved in neurotoxicity, drug metabolism and Alzheimer’s disease. SAR and QSAR in Environmental Research, 23(7-8), 627-647 (2012)
- I.G. Tikhonova, I.G.; I.I. Baskin, V.A. Palyulin, et al. Structural Basis for Understanding Structure-Activity Relationships for the Glutamate-Binding Site of NMDA Receptor. Journal of Medicinal Chemistry, 45(18), 3836-3843 (2002)
- I.I. Baskin, E.V. Gordeeva, R.O. Devdariani, et al. Methodology of the Inverse Problem Solution for the Structure Property Relation in Case of Topological Indexes. Doklady Chemistry, 307(3), p.217-220 (1989)