Dr. Igor Baskin

A. Academic Background

Igor Baskin academic background
2010Habilitation in computational chemistryLomonosov Moscow State University, RussiaModeling properties of chemical compounds using artificial neural networks and fragment descriptors
1990PhD in organic chemistryLomonosov Moscow State University, RussiaComputer-assisted systematic generation and analysis of organic reactions
1984MSc in chemistryLomonosov Moscow State University, RussiaTheoretical conformational analysis

B. Previous Employment

Igor Baskin previous employment
2021-presentTechnion – Israel Institute of Technology (Haifa, Israel)Senior researcherAI and data science in electrochemistry, first principles modeling and atomistic simulations
2013-2020Faculty of Physics, Lomonosov Moscow State University (Russia)Leading researcherChemoinformatics and materials informatics
2015-2020Butlerov Institute of Chemistry, Kazan Federal University, Russia (part-time)Senior researcherChemoinformatics, machine learning
2005-2020Faculty of Chemistry, Strasbourg University, France (multiple times)Visiting scientist, invited professorChemoinformatics, machine learning
2001-2013Department of Chemistry, Lomonosov Moscow State University, RussiaSenior and leading researcherMolecular modeling, medicinal chemistry, chemoinformatics
1994-2001Zelinsky Institute of Organic Chemistry, Moscow, RussiaSenior researcherComputational chemistry, neural networks
1988-1994Semenov Institute of Chemical Physics, Moscow, USSR and RussiaResearch engineer and researcherQuantum chemistry and atomistic simulations, photochromic dyes, mathematical chemistry, chemical databases, QSAR

C. Research Experience

  1. Chemoinformatics and materials informatics
  2. Artificial intelligence, machine learning (neural networks and deep learning, kernel-based and Bayesian methods, etc) and data science in chemistry and materials science
  3. Relationships between structures and properties of chemical compounds (QSAR/QSPR)
  4. Computational and medicinal chemistry, molecular modeling of supramolecular complexes, proteins and protein-ligand interactions, docking, molecular dynamics, virtual screening, de novo molecular design
  5. Mathematical chemistry, chemical graph theory
  6. 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

  1. 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)
  2. 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)
  3. E.N. Muratov, J. Bajorath, R.P. Sheridan, et al. QSAR without borders. Chemical Society Reviews, 49(11), 3525-3564 (2020)
  4. 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)
  5. 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)
  6. 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)
  7. 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)
  8. 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)
  9. 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)
  10. A. Varnek, I. Baskin. Chemoinformatics as a Theoretical Chemistry Discipline. Molecular Informatics, 30(1), 20-32 (2011)
  11. 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)
  12. 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)
  13. 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)
  14. 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)
  15. 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)
  16. 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)
  17. 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)
  18. 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)
  19. 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)
  20. 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)
  21. 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)
  22. 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)
  23. 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)
  24. 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)
  25. 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)
  26. 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)
  27. 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)
  28. 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)
  29. 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)
  30. 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)
  31. 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)
  32. I. Baskin, A. Varnek. Building a chemical space based on fragment descriptors. Comb. Chem. High Throughput Screening, 11(8), 661-668 (2008)
  33. 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)
  34. 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)
  35. 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)
  36. 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)