about
QSAR modeling: where have you been? Where are you going to?Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical informationThe One-Class Classification Approach to Data Description and to Models Applicability Domain.Generative Topographic Mapping (GTM): Universal Tool for Data Visualization, Structure-Activity Modeling and Dataset Comparison.Combined QSAR studies of inhibitor properties of O-phosphorylated oximes toward serine esterases involved in neurotoxicity, drug metabolism and Alzheimer's disease.Chemical data visualization and analysis with incremental generative topographic mapping: big data challenge.Building a chemical space based on fragment descriptors.Energy-Based Neural Networks as a Tool for Harmony-Based Virtual Screening.A renaissance of neural networks in drug discovery.Chemoinformatics as a Theoretical Chemistry Discipline.Synthesis and SAR requirements of adamantane-colchicine conjugates with both microtubule depolymerizing and tubulin clustering activities.GTM-Based QSAR Models and Their Applicability Domains.Transductive Support Vector Machines: Promising Approach to Model Small and Unbalanced Datasets.Kinetics and mechanism of inhibition of serine esterases by fluorinated aminophosphonates.Predicting ligand binding modes from neural networks trained on protein-ligand interaction fingerprints.The learned symmetry concept in revealing quantitative structure-activity relationships with artificial neural networks.Computer simulation of the three-dimensional structure of the glutamate site of the NR2B subunit of the NMDA receptor.A spatial model of the glycine site of the NR1 subunit of NMDA-receptor and ligand docking.A new binding mode of competitive antagonists to metabotropic glutamate receptors exemplified by the mGluR1-receptor antagonist AIDA (RS-aminoindan-1,5-dicarboxylic acid).Structural basis for understanding structure-activity relationships for the glutamate binding site of the NMDA receptor.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.Comparative analysis of the ligand-binding sites of the metabotropic glutamate receptors mGluR1-mGluR8.Molecular modeling the human A1 adenosine receptor and study of the mechanisms of its selective ligand binding.CoMFA and homology-based models of the glycine binding site of N-methyl-d-aspartate receptor.A quantitative model of ligand binding to the glutamate site of the GluR2 subunit of AMPA receptor.Molecular modeling of the human A2a adenosine receptor.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.Selectivity fields: comparative molecular field analysis (CoMFA) of the glycine/NMDA and AMPA receptors.The study of the mechanism of binding of human ML1A melatonin receptor ligands using molecular modeling.3D-model of the ion channel of NMDA receptor: qualitative and quantitative modeling of the blocker binding.Molecular modeling of N-terminal domains of NMDA-receptor. Study of ligand binding to N-terminal domains.The continuous molecular fields approach to building 3D-QSAR models.Machine learning methods for property prediction in chemoinformatics: Quo Vadis?Inductive transfer of knowledge: application of multi-task learning and feature net approaches to model tissue-air partition coefficients.Exhaustive QSPR studies of a large diverse set of ionic liquids: how accurately can we predict melting points?Molecular modeling study of the mechanism of ligand binding to human melatonin receptors.Molecular modeling and molecular dynamics simulation of the human A2B adenosine receptor. The study of the possible binding modes of the A2B receptor antagonists.Role of two chloride-binding sites in functioning of testicular angiotensin-converting enzyme.Stargate GTM: Bridging Descriptor and Activity Spaces.Quantitative structure-property relationship modeling: a valuable support in high-throughput screening quality control.
P50
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P50
description
Russisch onderzoeker
@nl
hulumtues
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researcher
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taighdeoir
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российский биохимик
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հետազոտող
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name
Igor Baskin
@fr
Igor I. Baskin
@en
Igor I. Baskin
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Igor I. Baskin
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Igor I. Baskin
@sl
Igor Iossifowitsch Baskin
@de
Игорь Баскин
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type
label
Igor Baskin
@fr
Igor I. Baskin
@en
Igor I. Baskin
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Igor I. Baskin
@nl
Igor I. Baskin
@sl
Igor Iossifowitsch Baskin
@de
Игорь Баскин
@ru
altLabel
Igor Baskin
@de
Igor Baskin
@en
Igor Iosifovich Baskin
@en
Баскин, Игорь И.
@ru
Игорь И. Баскин
@ru
Игорь Иосифович Баскин
@ru
prefLabel
Igor Baskin
@fr
Igor I. Baskin
@en
Igor I. Baskin
@es
Igor I. Baskin
@nl
Igor I. Baskin
@sl
Igor Iossifowitsch Baskin
@de
Игорь Баскин
@ru
P1053
I-2490-2012
P106
P1153
7005310411
P1412
P21
P2456
P27
P31
P3829
P496
0000-0003-0874-1148