My Scientific Work
My main scientific activity is devoted to the
development and application of informatics and machine learning methods (neural
networks, SVM, etc) to solve different problems in theoretical chemistry, as
well as to molecular modeling. The covered scientific areas include
chemoinformatics, SAR/QSAR/QSPR studies, neuroinformatics, mathematical
chemistry, medicinal chemistry, quantum chemistry and force-field molecular
modeling in organic chemistry, biological and supramolecular chemistry. The
most important of my scientific achievements are: (a) formulation of the
“inverse” QSAR/QSPR problem; (b) neural network for “direct” structure-property correlations; (c)
mathematical theory of fragmental descriptors, (d) universal approach for
predicting properties of organic compounds by using neural networks in
combination with fragmental descriptors; (e) molecular models of glutamate
receptors and their interactions with drug molecules, (f) one-class
classification as a universal approach to conducting virtual screening, (g) the
method of continuous molecular fields; (h) defining chemoinformatics as
theoretical chemistry discipline.
Developments
in theoretical conformational analysis
As a part of my Diploma work (Master Thesis), I
developed a method for describing conformational spaces of 8-, 9- and
10-membered rings in the framework of the Cremer-Pople approach and constructed
the corresponding maps.1 Using original approach, I also derived
the complete set of all canonical types of conformations for cyclic molecules.2
Development
of methods for mathematical description, systematic enumeration and computer
generation of organic reactions and their mechanisms
As
a part of my PhD studies, I developed methodology, algorithms and computer
program SYMBEQ for systematic generation and search for novel types of organic
reaction on the basis of Zefirov-Tratch formal-logical approach.3 Using this program, I systematically explored all organic reactions
with open4 and linear-cyclic5 topologies of bond redistribution. Novel reactions designed using the
SYMBEQ program were verified experimentally, and a new reaction (reaction of
dimethyl selenoxide with the ester of acetylenedicarboxylic acid leading to
formation of furan tetracarboxylic acid) was found.3 I also extended the formal-logical approach to description and
computer generation of reaction mechanisms, especially catalytic mechanisms.6 In the course of this work, I also developed an algorithm and computer
software for structural-fragment searches,7 along with the universal computer graphics program for organic
chemistry MOLED,8 which were used by me further for developing the STAR software for
building correlations structure-property.9
Molecular
modeling of self-assembling photo-switchable supramolecular devices
The
first part of my postdoc work in the Photochemistry Center of Russian Academy
of Sciences dealt with molecular modeling and quantum chemistry studies of crown-containing
styryl dyes. I simulated conformations and electronic absorption spectra of
crown-containing styryl dyes and their complexes with metal cations.10-12 Then I conducted a molecular mechanics study of regio- and
stereo-selectivity of cation-dependent photochemical [2+2]-autocycloaddition of
crown-containing styryl dyes.13, 14 As a further development of these studies, I studies, by combining
force-field molecular modeling with quantum chemistry calculations, regio- and
stereo-selectivity of the cation-dependent photochemical [2+2]-autocycloaddition
leading to formation of several types of self-assembling photo-switchable
molecular devices as well as their electronic absorption spectra: multiphotochromic
15-crown-5 ethers with rigid spacers and their anion-capped complexes,15, 16 photoswitchable molecular pincers,17 photoswitchable receptors based on dimeric complexes of styryl dyes
containing 15-crown-5 ether unit.18 Another of my works related to supramolecular chemistry dealt with
molecular dynamics study of cyclodextrine complexation.19
Molecular modeling of different proteins and
protein-ligand complexes
The experience in modeling supramolecular
complexes allowed proceeding to the modeling of proteins and their complexes
with organic ligands. Using combination of comparative protein modeling with
molecular mechanics and dynamics, I started with modeling of a neurotrophic
factor fragment from pigment epithelium.20, 21 After that I conducted a big cycle
of studies aimed at building spatial models of human glutamate receptors, understanding
structure-activity relationships from the structural point of view, performing
virtual screening and drug discovery.22 The following macromolecules were
explored: aminoterminal domain of glutamate metabotropic receptor mGluR1,23-25 ion channel inside the NMDA
receptor,26, 27 glutamate-binding sites of the
metabotropic glutamate receptors mGluR1-mGluR8,28 ligand-binding domain of the
kainite receptor.29 glutamate-binding,30 glycine-binding31, 32 and N-terminal33 domains of the NMDA receptor,
ligand-binding site of the GluR2 subunit of the AMPA-receptor.34 Results of these studies were used in
virtual screening35 and design of neuroprotective and
cognition-enhancing drugs.26 Predictions concerning the
structure of the aminoterminal domain of the mGluR1 receptor and the binding
modes of its ligands were further validated in experimental studies. Based on
these studies, I have also formulated the selectivity fields concept,32 which is currently rather popular
in medicinal chemistry. I also took part in molecular modeling of adenosine,36-39 melatonin,40-42 and WNT-protein binding FZD43-45 receptors and their interactions
with ligands. Another important object of my molecular modeling studies was
angiotensin-converting enzyme.46-49 My recent studies dealt with
modeling interactions between taxol and colchicine analogs with the goal of
discovering anti-cancer drugs.50, 51
Development
of algorithms and programs for handling chemical information in databases
In parallel with modeling photochromic dyes, I
also developed software for handling spectral databases of dyes based on my
original approaches and algorithms for handling chemical structures, properties
and their light absorption spectra.52, 53 Besides, I created Java-based
three-tier client-server system for handling databases of chemical structures,
along with Java library and Tcl/Tk platform for handling chemical information
based on original approaches.
Inverse problem in QSPR studies
In the joint work with Dr. M. Skvortsova, we
formulated for the first time the “inverse problem” – the task of generation of
chemical structures for given values of their properties, and solved it for
several cases.54-58 All solutions were based on
correlations of properties with topological indexes, and combination of graph
generation with application of graph theory to minimize the space of solutions
was used to generate chemical structures. Nowadays this is very hot area in
chemoinformatics, and the article56 is my most cited paper.
Neural device for finding direct structure-property
correlations
In 1993 I developed a special neural device for
building direct correlations between structures and properties of chemical
compounds without the need to pre-calculate molecular descriptors.59, 60 This neural network of special
architecture works directly with molecular graphs and extracts from them latent
non-linear features which are the most useful for predicting the property under
study. Very good performance of this device was demonstrated for predicting 7
different physicochemical properties. This work can be considered innovate not
only in chemoinformatics, but also in machine learning, because first
publications on graph-based data mining and machine learning with structured
data appeared later.
Theorems on the basis of invariants of
molecular graphs and fragmental approach
To address the problem of designing or choosing
the optimal sets of molecular descriptors for predicting properties of chemical
compounds, I formulated and proved (in cooperation with Dr. M. Skvortsova)
several theorems on the basis of invariants of labeled graphs.61-63 According to these theorems, any
molecular graph invariant (that is any molecular descriptor or scalar property)
can be uniquely represented as (1) a linear combination of the occurrence
numbers of some substructures (fragments), both connected and disconnected, or
(2) a polynomial on occurrence numbers of connected substructures of
corresponding molecular graph (which are the values of fragmental descriptors).
These results were used for formulating a methodology of constructing general
models for structure-property relationships at the topological level64 and a unified method to construct
linear equations for structure-property relations.65 In a later publication, it was also
shown that any metric in chemical space (that is any similarity measure between
chemical structures) can be expressed through fragmental descriptors.66, 67 Besides, a subset of fragmental
descriptors providing unique coding of chemical structures was found.68
The
main impact of the theorems on the basis of invariants of molecular graphs is
that they allowed substantiating the fragmental approach in chemoinformatics.69, 70 Based on these results, I developed
a set of fragmental descriptors based on hierarchical scheme of atom
classification,71, 72 as well as algorithms and software
(program FRAGMENT) for its efficient computation. Although the primary goal of
these descriptors was to predict physico-chemical properties of organic
compounds, they can successfully be applied to predicting biological activity,
including the assignment of organic compounds to pharmacological groups73 and mutagenicity.74 This set of descriptors was
successfully used by us for building models to predict numerous properties of
organic compounds, including the enthalpy of sublimation,75 flash point,76 molecular polarizability,77 magnetic susceptibility,78 affinity of dyes for cellulose
fiber,79 the stability of complexes with
α-cyclodextrin,80 the enthalpy of vaporization,81 lipophilicity,82 Abraham constants,82 melting points of ionic liquids.83 As a further development of the
fragmental approach, I developed fragmental descriptors with labeled atoms.84 Using this type of fragmental
descriptors, I succeeded in developing models to make quantitative predictions
of several types of biological activity,84-86 reaction rate constant for ester
hydrolysis84 and several types of local
properties, including 31P NMR shift,84 ionization constants,87 different substituent constants.88 As another direction of the
development of the fragmental approach, I put forward the concept of
pseudofragmental descriptors (FragProp), the values of which are combination of
certain properties of atoms forming the fragments.89 The advantages of using
pseudofragmental descriptors were demonstrated by the example of predicting
physical properties of polymers.89
Methodological developments in the use of
neural networks in chemoinformatics
I started to work with artificial neural
networks in 1993 with an article,90 in which for the first time neural
networks was used to predict physical properties of organic compounds. At that
time neural network was a new tool, and it was not clear how to: (1) prevent
overtraining; (2) handle stochastic properties of neural networks; (3) work
with a big number of descriptors; (4) interpret neural network models; (4) build
models with required symmetry properties. To solve the first problem, in 1995 I
suggested splitting data into three sets: (i) a training set used for learning;
(ii) a validation set used to define a point for early stopping of learning;
(iii) a test set used for assessing predictive performance of the neural
network model.91 The stochastic properties of neural
networks were addressed by using ensembles of neural networks, advantages of
which over separate neural network models were demonstrated by us for the case
of predicting physical properties of organic compounds.72 Further development of the ensemble
modeling idea in the framework of the three-set approach resulted in the creation
of the double cross-validation procedure,84 which became a standard routine for
all out neural network studies. To address the problem of a big quantity of
descriptors I have developed the Fast Stagewise Multiple Linear Regression (FSMLR)
procedure,84 which can efficiently select
descriptors even from a huge number of them (millions). Besides, FSMLR can be
used as independent machine learning method for the rapid construction of
linear regressions. Currently this method is available as a part of the OCHEM
project.92 The problem of the interpretability
of neural network regression models was solved by means of putting forward an approach
based on rapid calculation of first and second derivatives of outputs with
regard to inputs.93 The problem of proper symmetry of
neural network models was solved by suggesting the concept of the learned
symmetry,94 in accordance with which neural
networks learn the required symmetry properties during training. I wrote
several reviews concerning the use of neural networks in chemoinformatics95-97 and developed software NASAWIN,98 which was used in most of my
studies in this field.
Application of neural networks in conjunction
with fragmental descriptors
It follows from the aforementioned theorems on
the basis of invariants of molecular graphs and the Kolmogorov theorem in
neural network interpretation that any scalar property of chemical compound can
be approximated by a multi-layered neural network with inputs fed by fragmental
descriptors. This suggests theoretically the best combination of machine
learning method and molecular descriptor type. The first evidence that such
combination could be optimal for predicting physical properties of hydrocarbons
was obtained by me in the paper.90 The same conclusion was drawn for
big datasets for the case of predicting magnetic susceptibility,78 the enthalpy of vaporization,81 enthalpy of sublimation,75 flash point.76 The same conclusion was also made
for very diverse datasets with physical properties of organic compounds.72 Finally, this was supported in the
benchmark study of predicting the melting point of ionic liquids.83
New integrated concepts in the use of neural
networks in chemoinformatics
In order to more fully disclose the potential
applications of neural networks in chemoinformatics, I proposed three
integrated approaches. The first one, called QSCPR (Quantitative
Structure-Conditions-Property Relationships),99, 100 is designed to predict properties
of chemical compounds under at different conditions (e.g. the temperature, the
pressure, the properties of solvents, etc). I have shown that by mixing
descriptions of chemical structures and conditions with the help of
backpropagation neural networks, it is possible to model the “structure-pressure-boiling
point”, the “structure-temperature-density” and the “structure-temperature-viscosity”
relationships for hydrocarbons,99 as well as the “structure –
reaction conditions – rate constants” relationships for the acid hydrolysis of
esters.100 As a further development, a concept
of the “bimolecular” QSPR,101 in which descriptions of a pair of structures are combined with the help of
neural networks, was suggested. The efficiency of this approach was
demonstrated by predicting the solvation free energy of different compounds in
different solvents with a single model.101
The second integrated approach deals with
integration of the QSPR approach based on the use of neural networks with the results
of molecular modeling taken in the form of quantum chemical descriptors. I have
shown that in certain cases, when the amount of experimental data is not too
small and not too big, such combination can lead to very efficient solutions.
The advantages of this integrated approach were demonstrated for predicting:
(i) the position of the long-wave absorption band of symmetrical cyanine dyes
in alcohol solution,102 ionization constants for different
classes of organic compounds,87 and mutagenicity.103-105
The third integrated approach deals with integration
of different neural network QSPR models built for different but mutually
interrelated endpoints in the framework of the “inductive knowledge transfer”
concept. Efficiency of the “horizontal” integration of models (implemented
through the use of neural networks and multi-task learning) was demonstrated
(in cooperation with Prof. A. Varnek) for predicting a set of ADME properties.106 Efficiency of the “vertical”
integration of neural network QSPR models in the frame of the “multilevel
approach to the prediction of properties of organic compounds”82 was shown for predicting soil
sorption coefficients of organic compounds and solubility of fullerene C60
in different organic solvents.82
Main directions of my current work in
chemoinformatics
The first direction of my current work deals
with the use of one-class classification machine learning technique in
chemoinformatics. The possibility to use the one-class SVM method for defining
applicability domains for QSPR models was shown (in cooperation with Prof. A.
Varnek) for predicting stability constants of organic ligands with
alkaline-earth metals in water.107 I have also demonstrated high
efficiency of this approach for conducting ligand-based virtual screening.108-110 The next direction of my work deals
with development of the “continuous molecular fields” approach,110-112 which is based on representing
chemical structures in SAR/QSAR studies by means of continuous molecular field
functions instead of traditional discrete sets of descriptors. The third
direction of my work deals with the use of the Generative Topographic Mapping
(GTM) technique as a universal tool for chemical data visualization, structure-property
modeling and dataset comparison (in cooperation with Prof. A. Varnek).113 The fourth direction of my work
deals with defining the chemical space, development of methods to work with it,
and defining chemoinformatics as theoretical chemistry discipline (in
cooperation with Prof. A. Varnek).69, 114 We have formulated the main
mathematical problems in chemoinformatics and the ways to solve them in the
perspective review article.115 In 2011-2012, the papers114, 115 were the most accessed articles in two
top journals in the field of chemoinformatics.
References
1. Zotov, A. Y.; Baskin, I. I.; Palyulin,
V. A.; Zefirov, N. S. Quantitative characteristics of nine-membered ring
conformations. Journal of Chemical
Research, Synopses 1995, (4), 130-1.
2. Baskin,
I. I.; Palyulin, V. A.; Zefirov, N. S. Methodology for derivation of a complete
set of canonical types of conformations for cyclic molecules. Doklady Akademii Nauk 1992, 326 (5), 821-6 [Chem ].
3. Zefirov,
N. S.; Baskin, I. I.; Palyulin, V. A. SYMBEQ Program and Its Application in
Computer-Assisted Reaction Design. Journal
of Chemical Information and Computer Sciences 1994, 34 (4), 994-99.
4. Trach,
S. S.; Baskin, I. I.; Zefirov, N. S. Computers and molecular design problems.
XIII. Systematic analysis of organic processes characterized by open topologies
of redistributed bonds. Zhurnal
Organicheskoi Khimii 1988, 24 (6), 1121-33.
5. Trach,
S. S.; Baskin, I. I.; Zefirov, N. S. Problems of molecular design and
computers. XIV. Systematic analysis of organic processes, characterized by
linear-cyclic topology of bond redistribution. Zhurnal Organicheskoi Khimii 1989,
25 (8), 1585-606.
6. Zefirov,
N. S.; Baskin, I. I. Problems of molecular design and computers. XV.
Description of reaction mechanisms within the framework of a formal-logic
approach. Zhurnal Organicheskoi Khimii 1993, 29 (3), 449-60.
7. Stankevich,
M. I.; Baskin, I. I.; Zefirov, N. S. Automation of structural-fragment
searches. Algorithm and computer programs. Zhurnal
Strukturnoi Khimii 1987, 28 (6), 136-7.
8. Zefirov,
N. S.; Baskin, I. I.; Trach, S. S. Universal computer graphics program for
organic chemistry purposes. Zhurnal
Vsesoyuznogo Khimicheskogo Obshchestva im. D. I. Mendeleeva 1987, 32 (1), 112-13.
9. Baskin,
I. I.; Stankevich, M. I.; Devdariani, R. O.; Zefirov, N. S. Program complex for
structure-property correlations based on topological indexes. Zhurnal Strukturnoi Khimii 1989, 30 (6), 145-7.
10. Baskin,
I. I.; Burshtein, K. Y.; Bagatur'yants, A. A.; Gromov, S. P.; Alfimov, M. V.
Molecular simulation of conformation and electronic absorption spectra of
crown-containing styryl dyes and their complexes with metal cations. Doklady Akademii Nauk 1992, 325 (2), 306-10 [Phys. Chem.].
11. Baskin,
I. I.; Burshtein, K. Y.; Bagatur'yants, A. A.; Gromov, S. P.; Alfimov, M. V.
Molecular simulation of the complexation effects on conformations and
electronic absorption spectra of crown ether styryl dyes. Journal of Molecular Structure 1992,
274, 93-104.
12. Baskin,
I. I.; Burshtein, K. Y.; Bagatur'yance, A. A.; Gromov, S. P.; Alfimov, M. V.
Molecular simulation of the influence of complexing on the conformation and
electronic absorption spectra of crown-containing styryl dyes. Zhurnal Strukturnoi Khimii 1993, 34 (2), 39-45.
13. Baskin,
I. I.; Bagatur'yants, A. A.; Gromov, S. P.; Alfimov, M. V. Molecular mechanics
study of regio- and stereoselectivity of cation-dependent photochemical
[2+2]-autocycloaddition of crown-containing styryl dyes. Doklady Akademii Nauk 1994,
335 (3), 313-16.
14. Baskin,
I. I.; Freidzon, A. Y.; Bagatur'yants, A. A.; Gromov, S. P.; Alfimov, M. V.
Application of molecular mechanics to the study of regio- and stereoselectivity
of cation-dependent [2+2]-photocycloaddition in crown ether styryl dyes. Internet Journal of Chemistry [Electronic
Publication] 1998, 1, No pp. given Article 19.
15. Gromov,
S. P.; Fedorova, O. A.; Ushakov, E. N.; Baskin, I. I.; Lindeman, A. V.;
Malysheva, E. V.; Balashova, T. A.; Arsen'ev, A. S.; Alfimov, M. V. Crown ether
styryl dyes. 24. Synthesis of multiphotochromic 15-crown-5 ethers with rigid
spacers, their anion-\"capped\" complexes, and stereospecific [2+2]
autophotocycloaddition. Russian Chemical
Bulletin (Translation of Izvestiya Akademii Nauk, Seriya Khimicheskaya) 1998, 47 (1), 97-106.
16. Ushakov,
E. N.; Gromov, S. P.; Buevich, A. V.; Baskin, I. I.; Fedorova, O. A.;
Vedernikov, A. I.; Alfimov, M. V.; Eliasson, B.; Edlund, U. Crown-containing
styryl dyes: cation-induced self-assembly of multiphotochromic 15-crown-5
ethers into photoswitchable molecular devices. Journal of the Chemical Society, Perkin Transactions 2: Physical
Organic Chemistry 1999, (3), 601-608.
17. Gromov,
S. P.; Fedorova, O. A.; Ushakov, E. N.; Buevich, A. V.; Baskin, I. I.;
Pershina, Y. V.; Eliasson, B.; Edlund, U.; Alfimov, M. V. 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 the
Chemical Society, Perkin Transactions 2: Physical Organic Chemistry 1999,
(7), 1323-1329.
18. Gromov,
S. P.; Ushakov, E. N.; Fedorova, O. A.; Baskin, I. I.; Buevich, A. V.;
Andryukhina, E. N.; Alfimov, M. V.; Johnels, D.; Edlund, U. G.; Whitesell, J.
K.; Fox, M. A. 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 2003, 68 (16), 6115-6125.
19. Kazachinskaya,
E. P.; Baskin, I. I.; Mamonov, P. A.; Matveenko, V. N. Molecular simulation of
complexation of ОІ-cyclodextrin and vitamin K3 molecules. Moscow University Chemistry Bulletin 2006, 61 (4), 36-42.
20. Kostanian,
I. A.; Zhokhov, S. S.; Astapova, M. V.; Dranitsyna, S. M.; Bogachuk, A. P.;
Baidakova, L. K.; Rodionov, I. L.; Baskin, I. I.; Golubeva, O. N.;
Tombran-Tink, J.; Lipkin, V. M. Biological role of a neurotrophic factor
fragment from pigment epithelium: structure-functional homology with a
differentiation factor for the HL-60 cell line. Bioorganicheskaia khimiia 2000,
26 (8), 563-70.
21. Kostanyan,
I. A.; Zhokhov, S. S.; Astapova, M. V.; Dranitsyna, S. M.; Bogachuk, A. P.;
Baidakova, L. K.; Rodionov, I. L.; Baskin, I. I.; Golubeva, O. N.;
Tombran-Tink, J.; Lipkin, V. M. The biological function of a fragment of the
neurotrophic factor from pigment epithelium: structural and functional homology
with the differentiation factor of the HL-60 cell line. Russian Journal of Bioorganic Chemistry (Translation of
Bioorganicheskaya Khimiya) 2000,
26 (8), 505-511.
22. Baskin,
I. I.; Palyulin, V. A.; Zefirov, N. S. Molecular simulation of receptors of
physiologically active compounds for purposes of medical chemistry. Russ. Chem. Rev. 2009, 78 (6), 495-511.
23. Baskin,
I. I.; Belenikin, M. S.; Ekimova, E. V.; Costantino, G.; Palyulin, V. A.;
Pellicciari, R.; Zefirov, N. S. Molecular modeling of aminoterminal domain of
glutamate metabotropic receptor mGluR1. Doklady
Akademii Nauk 2000, 374 (3), 347-351.
24. Belenikin,
M. S.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Molecular modeling of the
amino-terminal domain of the mGluR1 glutamate metabotropic receptor by the
threading method. Doklady Chemistry
(Translation of the chemistry section of Doklady Akademii Nauk) 2002, 383 (4-6), 97-101.
25. Belenikin,
M. S.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. A new binding mode of
competitive antagonists to metabotropic glutamate receptors exemplified by the
mGluR1-receptor antagonist AIDA (RS-aminoidan-1,5-dicarboxylic acid). Doklady Biochemistry and Biophysics 2002, 384, 131-135.
26. Bachurin,
S.; Tkachenko, S.; Baskin, I.; Lermontova, N.; Mukhina, T.; Petrova, L.;
Ustinov, A.; Proshin, A.; Grigoriev, V.; Lukoyanov, N.; Palyulin, V.; Zefirov,
N. Neuroprotective and cognition-enhancing properties of MK-801 flexible
analogs: Structure-activity relationships. Annals
of the New York Academy of Sciences 2001,
939 (Neuroprotective Agents),
219-236.
27. Tikhonova,
I. G.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. 3D-Model of the ion
channel of NMDA receptor: Qualitative and quantitative modeling of the blocker
binding. Doklady Biochemistry and
Biophysics 2004, 396, 181-186.
28. Belenikin,
M. S.; Baskin, I. I.; Costantino, G.; Palyulin, V. A.; Pellicciari, R.;
Zefirov, N. S. Comparative analysis of the ligand-binding sites of the
metabotropic glutamate receptors mGLuR1-mGluR8. Doklady Biological Sciences 2002,
386, 251-256.
29. Belenikin,
M. S.; Baskin, I. I.; Costantino, G.; Palyulin, V. A.; Pellicciari, R.;
Zefirov, N. S. Molecular modeling of the closed forms of the kainate-binding
domains of kainate receptors and qualitative analysis of the structure-activity
relationships for some agonists. Doklady
Biological Sciences 2002, 386, 239-244.
30. Tikhonova,
I. G.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S.; Bachurin, S. O.
Structural Basis for Understanding Structure-Activity Relationships for the
Glutamate Binding Site of the NMDA Receptor. Journal of Medicinal Chemistry 2002,
45 (18), 3836-3843.
31. Tikhonova,
I. G.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. A spatial model of the
glycine site of the NR1 subunit of NMDA-receptor and ligand docking. Doklady Biochemistry and Biophysics 2002, 382, 67-70.
32. Baskin,
I. I.; Tikhonova, I. G.; Palyulin, V. A.; Zefirov, N. S. Selectivity Fields:
Comparative Molecular Field Analysis (CoMFA) of the Glycine/NMDA and AMPA
Receptors. J. Med. Chem. 2003, 46 (19), 4063-4069.
33. Tikhonova,
I. G.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Molecular modeling of
N-terminal domains of NMDA-receptor. Study of ligand binding to N-terminal
domains. Doklady Biochemistry and
Biophysics 2004, 397, 242-250.
34. Tikhonova,
I. G.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. A quantitative model of
ligand binding to the glutamate site of the GluR2 subunit of AMPA receptor. Doklady Biochemistry and Biophysics 2003, 389, 75-78.
35. Tikhonova,
I. G.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Virtual screening of
organic molecule databases. Design of focused libraries of potential ligands of
NMDA and AMPA receptors. Russian Chemical
Bulletin (Translation of Izvestiya Akademii Nauk, Seriya Khimicheskaya) 2004, 53 (6), 1335-1344.
36. Ivanov,
A. A.; Baskin, I. I.; Palyulin, V. A.; Baraldi, P. G.; Zefirov, N. S. Molecular
modelling of the human A2b adenosine receptor and an analysis of the binding
modes of its selective ligands. Mendeleev
Communications 2002, (6), 211-212.
37. Ivanov,
A. A.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Molecular modeling the
human A1 adenosine receptor and study of the mechanisms of its selective ligand
binding. Doklady Biological Sciences 2002, 386, 271-274.
38. Ivanov,
A. A.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Molecular modeling of
adenosine receptors. Vestnik Moskovskogo
Universiteta, Seriya 2: Khimiya 2002,
43 (4), 231-236.
39. Ivanov,
A. A.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Molecular modeling of the
human A2a adenosine receptor. Doklady
Biochemistry and Biophysics 2003,
389, 94-97.
40. Ivanov,
A. A.; Voronkov, A. E.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. The
study of the mechanism of binding of human ML1A melatonin receptor ligands
using molecular modeling. Doklady
Biochemistry and Biophysics 2004,
394, 49-52.
41. Voronkov,
A. E.; Ivanov, A. A.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Molecular
Modeling Study of the Mechanism of Ligand Binding to Human Melatonin Receptors.
Doklady Biochemistry and Biophysics 2005, 403, 284-288.
42. Voronkov,
A. E.; Ivanov, A. A.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Molecular
modeling study of the mechanism of ligand binding to human melatonin receptors.
Doklady Biochemistry and Biophysics 2005, 403 (1-6), 284-288.
43. Voronkov,
A. E.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Molecular modeling of the
complex between the XWNT8 protein and the CRD domain of the MFZD8 receptor. Doklady Biochemistry and Biophysics 2007, 412 (1), 8-11.
44. Voronkov,
A. E.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Molecular modeling of
modified peptides, potent inhibitors of the xWNT8 and hWNT8 proteins. Journal of Molecular Graphics and Modelling 2008, 26 (7), 1179-1187.
45. Voronkov,
A. E.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Molecular model of the
Wnt protein binding site on the surface of dimeric CRD domain of the hFzd8
receptor. Doklady Biochemistry and
Biophysics 2008, 419 (1), 75-78.
46. Voronov,
S. V.; Bineskii, P. V.; Zueva, N. A.; Paliulin, V. A.; Baskin, I. I.; Orlova,
M. A.; Kost, O. A. Structure-functional features of homologous domains of
angiotensin-converting enzyme. Bioorganicheskaia
khimiia 2003, 29 (5), 470-8.
47. Voronov,
S. V.; Binevski, P. V.; Zueva, N. A.; Palyulin, V. A.; Baskin, I. I.; Orlova,
M. A.; Kost, O. A. Structural and functional peculiarities of homologous
domains of angiotensin-converting enzyme. Russian
Journal of Bioorganic Chemistry (Translation of Bioorganicheskaya Khimiya) 2003, 29 (5), 426-433.
48. Moiseeva,
N. A.; Binevski, P. V.; Baskin, I. I.; Palyulin, V. A.; Kost, O. A. Role of two
chloride-binding sites in functioning of testicular angiotensin-converting
enzyme. Biochemistry (Moscow) 2005, 70 (10), 1167-1172.
49. Skirgello,
O. E.; Balyasnikova, I. V.; Binevski, P. V.; Sun, Z. L.; Baskin, I. I.;
Palyulin, V. A.; Nesterovitch, A. B.; Albrecht Ii, R. F.; Kost, O. A.; Danilov,
S. M. Inhibitory antibodies to human angiotensin-converting enzyme: Fine
epitope mapping and mechanism of action. Biochemistry
2006, 45 (15), 4831-4847.
50. Nurieva,
E. V.; Semenova, I. S.; Nuriev, V. N.; Shishov, D. V.; Baskin, I. I.; Zefirova,
O. N.; Zefirov, N. S. Diels-alder reaction as a synthetic approach to
bicyclo[3.3.1]nonane colchicine analogs. Russian
Journal of Organic Chemistry 2010,
46 (12), 1892-1895.
51. Zefirova,
O. N.; Nurieva, E. V.; Shishov, D. V.; Baskin, I. I.; Fuchs, F.; Lemcke, H.;
Schroeder, F.; Weiss, D. G.; Zefirov, N. S.; Kuznetsov, S. A. Synthesis and SAR
requirements of adamantane-colchicine conjugates with both microtubule
depolymerizing and tubulin clustering activities. Bioorganic & Medicinal Chemistry 2011, 19 (18), 5529-5538.
52. Barachevskii,
V. A.; Ait, A. O.; Baskin, I. I.; Alfimov, M. V. Database development according
to structures and properties of organic photochromic compounds. Zhurnal Nauchnoi i Prikladnoi Fotografii 1996, 41 (4), 44-51.
53. Ait, A.
O.; Barachevsky, V. A.; Alfimov, M. V.; Baskin, I. I. Spectral data base on
photochromic organic compounds. Molecular
Crystals and Liquid Crystals Science and Technology, Section A: Molecular
Crystals and Liquid Crystals 1997,
298, 547-551.
54. Baskin,
I. I.; Gordeeva, E. V.; Devdariani, R. O.; Zefirov, N. S.; Palyulin, V. A.;
Stankevich, M. I. Solving the inverse problem of structure-property relations
for the case of topological indexes. Dokl.
Akad. Nauk SSSR 1989, 307 (3), 613-17.
55. Skvortsova,
M. I.; Baskin, I. I.; Slovokhotova, O. L.; Palyulin, V. A.; Zefirov, N. S.
Inverse problem in QSAR/QSPR [quantitative structure-property] studies for the
case of topological indexes, characterizing molecular shape (Kier indexes). Doklady Akademii Nauk 1992, 324 (2), 344-8 [Chem.].
56. Skvortsova,
M. I.; Baskin, I. I.; Slovokhotova, O. L.; Palyulin, V. A.; Zefirov, N. S.
Inverse problem in QSAR/QSPR studies for the case of topological indexes
characterizing molecular shape (Kier indices). J. Chem. Inf. Comput. Sci. 1993,
33 (4), 630-634.
57. Skvortsova,
M. I.; Baskin, I. I.; Palyulin, V. A.; Slovokhotova, O. L.; Zefirov, N. S.
Structural design. Inverse problems for topological indices in QSAR/QSPR
studies. AIP Conference Proceedings 1995, 330 (E.C.C.C. 1 Computational Chemistry), 486-99.
58. Skvortsova,
M. I.; Baskin, I. I.; Slovokhotova, O. L.; Palyulin, V. A.; Zefirov, N. S. The
inverse problem in structure-property relationship studies for the case of a
correlation equation containing arbitrary topological descriptors. Doklady Akademii Nauk 1996, 346 (4), 497-500.
59. Baskin,
I. I.; Palyulin, V. A.; Zafirov, N. S. Methodology of searching for direct
correlations between structures and properties of organic compounds by using
computational neural networks. Doklady
Akademii Nauk 1993, 333 (2), 176-9.
60. Baskin,
I. I.; Palyulin, V. A.; Zefirov, N. S. A Neural Device for Searching Direct
Correlations between Structures and Properties of Chemical Compounds. J. Chem. Inf. Comput. Sci. 1997, 37 (4), 715-721.
61. Baskin,
I. I.; Skvortsova, M. I.; Stankevich, I. V.; Zefirov, N. S. Basis of invariants
of labeled molecular graphs. Doklady
Akademii Nauk 1994, 339 (3), 346-50.
62. Baskin,
I. I.; Skvortsova, M. I.; Stankevich, I. V.; Zefirov, N. S. On the Basis of Invariants
of Labeled Molecular Graphs. J. Chem.
Inf. Comput. Sci. 1995, 35 (3), 527-31.
63. Skvortsova,
M. I.; Baskin, I. I.; Skvortsov, L. A.; Palyulin, V. A.; Zefirov, N. S.;
Stankevich, I. V. Chemical graphs and their basis invariants. Theochem 1999, 466, 211-217.
64. Skvortsova,
M. I.; Baskin, I. I.; Slovokhotova, O. L.; Zefirov, N. S. Methodology of
constructing general models of structure-property relations at the topological
level. Doklady Akademii Nauk 1994, 336 (4), 496-9.
65. Skvortsova,
M. I.; Baskin, I. I.; Stankevich, I. V.; Zefirov, N. S. Construction of linear
equations of structure-property relations. Doklady
Akademii Nauk 1996, 351 (1), 78-80.
66. Skvortsova,
M. I.; Stankevich, I. V.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. A.
Analytical description of the set of metric similarity measures of molecular
graphs. Doklady Akademii Nauk 1996, 350 (6), 786-788.
67. Skvortsova,
M. I.; Baskin, I. I.; Stankevich, I. V.; Palyulin, V. A.; Zefirov, N. S.
Molecular similarity. 1. Analytical description of the set of graph similarity
measures. J. Chem. Inf. Comput. Sci. 1998, 38 (5), 785-790.
68. Skvortsova,
M. I.; Fedyaev, K. S.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. A new
technique for coding chemical structures based on basis fragments. Doklady Chemistry (Translation of the
chemistry section of Doklady Akademii Nauk) 2002, 382 (4-6), 33-36.
69. Baskin,
I.; Varnek, A. Building a chemical space based on fragment descriptors. Comb. Chem. High T. Scr. 2008, 11 (8), 661-668.
70. Baskin,
I.; Varnek, A. 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. RSC Publisher: Cambridge, 2008; pp 1-43.
71. Artemenko,
N. V.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Prediction of Physical
Properties of Organic Compounds Using Artificial Neural Networks within the
Substructure Approach. Dokl. Chem. 2001, 381 (1-3), 317-320.
72. Artemenko,
N. V.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Artificial neural network
and fragmental approach in prediction of physicochemical properties of organic
compounds. Russian Chemical Bulletin
(Translation of Izvestiya Akademii Nauk, Seriya Khimicheskaya) 2003, 52 (1), 20-29.
73. Kondratovich,
E. P.; Zhokhova, N. I.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S.
Fragmental descriptors in (Q)SAR: Prediction of the assignment of organic
compounds to pharmacological groups using the support vector machine approach. Russ. Chem. Bull. 2009, 58 (4), 657-662.
74. Sushko,
I.; Novotarskyi, S.; Körner, R.; Pandey, A. K.; Cherkasov, A.; Li, J.;
Gramatica, P.; Hansen, K.; Schroeter, T.; MГјller, K. R.; Xi, L.; Liu, H.; Yao,
X.; Г–berg, T.; Hormozdiari, F.; Dao, P.; Sahinalp, C.; Todeschini, R.;
Polishchuk, P.; Artemenko, A.; Kuz'Min, V.; Martin, T. M.; Young, D. M.;
Fourches, D.; Muratov, E.; Tropsha, A.; Baskin, I.; Horvath, D.; Marcou, G.;
Muller, C.; Varnek, A.; Prokopenko, V. V.; Tetko, I. V. Applicability domains
for classification problems: Benchmarking of distance to models for ames
mutagenicity set. Journal of Chemical
Information and Modeling 2010, 50 (12), 2094-2111.
75. Zhokhova,
N. I.; Baskin, I. I.; Palyulin, V. A.; Zefirov, A. N.; Zefirov, N. S.
Calculation of the Enthalpy of Sublimation by the QSPR Method with the Use of a
Fragment Approach. Russian Journal of
Applied Chemistry (Translation of Zhurnal Prikladnoi Khimii) 2003, 76 (12), 1914-1919.
76. Zhokhova,
N. I.; Baskin, I. I.; Palyulin, V. A.; Zefirov, A. N.; Zefirov, N. S.
Fragmental descriptors in QSPR: flash point calculations. Russ. Chem. Bull. 2003, 52 (9), 1885-1892.
77. Zhokhova,
N. I.; Baskin, I. I.; Palyulin, V. A.; Zefirov, A. N.; Zefirov, N. S.
Fragmental descriptors in QSPR: application to molecular polarizability
calculations. Russian Chemical Bulletin
(Translation of Izvestiya Akademii Nauk, Seriya Khimicheskaya) 2003, 52 (5), 1061-1065.
78. Zhokhova,
N. I.; Baskin, I. I.; Palyulin, V. A.; Zefirov, A. N.; Zefirov, N. S. Fragment
descriptors in QSPR: Application to magnetic susceptibility calculations. Journal of Structural Chemistry 2004, 45 (4), 626-635.
79. Zhokhova,
N. I.; Baskin, I. I.; Palyulin, V. A.; Zefirov, A. N.; Zefirov, N. S. A Study
of the Affinity of Dyes for Cellulose Fiber within the Framework of a Fragment
Approach in QSPR. Russian Journal of
Applied Chemistry 2005, 78 (6), 1013-1017.
80. Zhokhova,
N. I.; Bobkov, E. V.; Baskin, I. I.; Palyulin, V. A.; Zefirov, A. N.; Zefirov,
N. S. Calculation of the stability of ОІ-cyclodextrin complexes of organic
compounds using the QSPR approach. Moscow
University Chemistry Bulletin 2007,
62 (5), 269-272.
81. Zhokhova,
N. I.; Palyulin, V. A.; Baskin, I. I.; Zefirov, A. N.; Zefirov, N. S. Fragment
descriptors in the QSPR method: Their use for calculating the enthalpies of
vaporization of organic substances. Russian
Journal of Physical Chemistry A 2007,
81 (1), 9-12.
82. Baskin,
I. I.; Zhokhova, N. I.; Palyulin, V. A.; Zefirov, A. N.; Zefirov, N. S.
Multilevel approach to the prediction of properties of organic compounds in the
framework of the QSAR/QSPR methodology. Doklady
Chemistry 2009, 427 (1), 172-175.
83. Varnek,
A.; Kireeva, N.; Tetko, I. V.; Baskin, I. I.; Solov'ev, V. P. Exhaustive QSPR
studies of a large diverse set of ionic liquids: How accurately can we predict
melting points? J. Chem. Inf. Mod. 2007, 47 (3), 1111-1122.
84. Zhokhova,
N. I.; Baskin, I. I.; Palyulin, V. A.; Zefirov, A. N.; Zefirov, N. S.
Fragmental descriptors with labeled atoms and their application in QSAR/QSPR
studies. Dokl. Chem. 2007, 417 (2), 282-284.
85. Palyulin,
V. A.; Radchenko, E. V.; Baskin, I. I.; Makhaeva, G. F.; Zefirov, N. S.
Modelling the multi-target selectivity: O-phosphorylated oximes as serine
hydrolase inhibitors. Chem. Central J. 2009, 3 (SUPPL. 1).
86. Makhaeva,
G. F.; Radchenko, E. V.; Baskin, I. I.; Palyulin, V. A.; Richardson, R. J.;
Zefirov, N. S. Combined QSAR studies of inhibitor properties of
O-phosphorylated oximes toward serine esterases involved in neurotoxicity, drug
metabolism and Alzheimer's disease. SAR
QSAR Environ. Res. 2012, 23 (7-8), 627-647.
87. Ivanova,
A. A.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Estimation of ionization
constants for different classes of organic compounds with the use of the
fragmental approach to the search of structure-property relationships. Doklady Chemistry 2007, 413 (2), 90-94.
88. Kurilo,
M. N.; Karpov, P. V.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Neural
network modeling of substituent constants on the basis of fragmental
descriptors. Doklady Chemistry 2010, 431 (1), 85-88.
89. Zhokhova,
N. I.; Baskin, I. I.; Zefirov, A. N.; Palyulin, V. A.; Zefirov, N. S.
Pseudofragmental descriptors based on combinations of atomic properties for
prediction of physical properties of polymers in quantitative
structure-property relationship studies. Doklady
Chemistry 2010, 430 (2), 39-42.
90. Baskin,
I. I.; Palyulin, V. A.; Zefirov, N. S. Computational neural networks as an
alternative to linear regression analysis in studies of quantitative
structure-property relationships for the case of the physiocochemical
properties of hydrocarbons. Doklady
Akademii Nauk 1993, 332 (6), 713-16.
91. Baskin,
I. I.; Skvortsova, M. I.; Palyulin Vladimir, A.; Zefirov Nikolai, S.
Quantitative Chemical Structure-Property/Activity Studies Using Artificial
Neural Networks. Foundations of Computing
and Decision Sciences 1997, 22 (2), 107-116.
92. Sushko,
I.; Pandey, A. K.; Novotarskyi, S.; Körner, R.; Rupp, M.; Teetz, W.;
Brandmaier, S.; Abdelaziz, A.; Prokopenko, V. V.; Tanchuk, V. Y.; Todeschini,
R.; Varnek, A.; Marcou, G.; Ertl, P.; Potemkin, V.; Grishina, M.; Gasteiger,
J.; Baskin, I. I.; Palyulin, V. A.; Radchenko, E. V.; Welsh, W. J.;
Kholodovych, V.; Chekmarev, D.; Cherkasov, A.; Aires-De-Sousa, J.; Zhang, Q.
Y.; Bender, A.; Nigsch, F.; Patiny, L.; Williams, A.; Tkachenko, V.; Tetko, I.
V. Online chemical modeling environment (OCHEM): Web platform for data storage,
model development and publishing of chemical information. Journal of Cheminformatics 2011,
3 (SUPPL. 1).
93. Baskin,
I. I.; Ait, A. O.; Halberstam, N. M.; Palyulin, V. A.; Zefirov, N. S. An
approach to the interpretation of backpropagation neural network models in QSAR
studies. SAR QSAR Environ. Res. 2002, 13 (1), 35-41.
94. Baskin,
I. I.; Halberstam, N. M.; Mukhina, T. V.; Palyulin, V. A.; Zefirov, N. S. The
learned symmetry concept in revealing quantitative structure-activity
relationships with artificial neural networks. SAR and QSAR in Environmental Research 2001, 12 (4), 401-416.
95. Baskin,
I. I.; Palyulin, V. A.; Zefirov, N. S. Application of artificial neuron nets to
chemical and biochemical investigations. Vestnik
Moskovskogo Universiteta, Seriya 2: Khimiya 1999, 40 (5), 323-326.
96. Halberstam,
N. M.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Neural networks as a
method for elucidating structure-property relationships for organic compounds. Russ. Chem. Rev. 2003, 72 (7), 629-649.
97. Baskin,
I. I.; Palyulin, V. A.; Zefirov, N. S. Neural networks in building QSAR models.
Methods Mol. Biol. 2008, 458, 137-158.
98. Baskin,
I. I.; Halberstam, N. M.; Artemenko, N. V.; Palyulin, V. A.; Zefirov, N. S.
NASAWIN – a universal software for QSPR/QSAR studies. In EuroQSAR 2002 Designing Drugs and Crop Protectants: processes, problems
and solutions., Ford, M., Ed. Blackwell Publishing: 2003; pp 260-263.
99. Gal'bershtam,
N. M.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Construction of
Neural-Network Structure-Condition-Property Relationships: Modeling of
Physicochemical Properties of Hydrocarbons. Doklady
Chemistry (Translation of the chemistry section of Doklady Akademii Nauk) 2002, 384 (1-3), 140-143.
100. Halberstam,
N. M.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Quantitative
structure-conditions-property relationship studies. Neural network modelling of
the acid hydrolysis of esters. Mendeleev
Communications 2002, (5), 185-186.
101. Kravtsov,
A. A.; Karpov, P. V.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S.
"bimolecular" QSPR: Estimation of the solvation free energy of
organic molecules in different solvents. Dokl.
Chem. 2007, 414 (1), 128-131.
102. Baskin,
I. I.; Ait, A. O.; Gal'bershtam, N. M.; Palyulin, V. A.; Alfimov, M. V.;
Zefirov, N. S. Use of artificial neural networks for predicting properties of
complex molecular systems. Prediction of the long-Wave absorption band of symmetrical
cyanine dyes. Doklady Akademii Nauk 1997, 357 (1), 57-59.
103. Baskin,
I. I.; Lyubimova, I. K.; Abliev, S. K.; Palyulin, V. A.; Zefirov, N. S.
Quantitative structure-activity relationship study of mutagenic activity of
chemical compounds. Substituted biphenyls. Doklady
Akademii Nauk 1993, 332 (5), 587-9.
104. Baskin,
I. I.; Lyubimova, I. K.; Abilev, S. K.; Palyulin, V. A.; Zefirov, N. S.
Quantitative relation between the mutagenic activity of heterocyclic analogs of
pyrene and phenanthrene and their structure. Doklady Akademii Nauk 1994,
339 (1), 106-8.
105. Lyubimova,
I. K.; Abilev, S. K.; Gal'berstam, N. M.; Baskin, I. I.; Palyulin, V. A.;
Zefirov, N. S. Computer-aided prediction of the mutagenic activity of
substituted polycyclic compounds. Biology
Bulletin (Moscow, Russian Federation (Translation of Izvestiya Rossiiskoi
Akademii Nauk, Seriya Biologicheskaya)) 2001, 28 (2), 139-145.
106. Varnek,
A.; Gaudin, C.; Marcou, G.; Baskin, I.; Pandey, A. K.; Tetko, I. V. 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 2009, 49 (1), 133-144.
107. Baskin,
I. I.; Kireeva, N.; Varnek, A. The One-Class Classification Approach to Data
Description and to Models Applicability Domain. Mol. Inf. 2010, 29 (8-9), 581-587.
108. Karpov,
P. V.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Virtual screening based
on one-class classification. Dokl. Chem. 2011, 437 (2), 107-111.
109. Karpov,
P. V.; Osolodkin, D. I.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S.
One-class classification as a novel method of ligand-based virtual screening:
The case of glycogen synthase kinase 3ОІ inhibitors. Bioorg. Med. Chem. Lett. 2011,
21 (22), 6728-6731.
110. Karpov,
P. V.; Baskin, I. I.; Zhokhova, N. I.; Zefirov, N. S. Method of continuous
molecular fields in the one-class classification task. Dokl. Chem. 2011, 440 (2), 263-265.
111. Zhokhova,
N. I.; Baskin, I. I.; Bakhronov, D. K.; Palyulin, V. A.; Zefirov, N. S. Method
of continuous molecular fields in the search for quantitative
structure-activity relationships. Doklady
Chemistry 2009, 429 (1), 273-276.
112. Karpov,
P. V.; Baskin, I. I.; Zhokhova, N. I.; Nawrozkij, M. B.; Zefirov, A. N.;
Yablokov, A. S.; Novakov, I. A.; Zefirov, N. S. One-class approach: models for
virtual screening of non-nucleoside HIV-1 reverse transcriptase inhibitors
based on the concept of continuous molecular fields. Russ. Chem. Bull. 2011, 60 (11), 2418-2424.
113. Kireeva,
N.; Baskin, I. I.; Gaspar, H. A.; Horvath, D.; Marcou, G.; Varnek, A.
Generative Topographic Mapping (GTM): Universal Tool for Data Visualization,
Structure-Activity Modeling and Dataset Comparison. Mol. Inf. 2012, 31 (3-4), 301-312.
114. Varnek,
A.; Baskin, I. I. Chemoinformatics as a Theoretical Chemistry Discipline. Mol. Inf. 2011, 30 (1), 20-32.
115. Varnek,
A.; Baskin, I. Machine Learning Methods for Property Prediction in
Chemoinformatics: Quo Vadis? J. Chem.
Inf. Mod. 2012, 52 (6), 1413-1437.