Vladimir A. Palyulin

发表

Ionotropic GABA receptors: modelling and design of selective ligands

Nikolai S. Zefirov, Vladimir I. Chupakhin, Vladimir A. Palyulin, 2010, J. Cheminformatics.

Inverse problem in QSAR/QSPR studies for the case of topological indexes characterizing molecular shape (Kier indices)

Igor I. Baskin, Nikolai S. Zefirov, Vladimir A. Palyulin, 1993, J. Chem. Inf. Comput. Sci..

New Developments in Hydrogen Bonding Acidity and Basicity of Small Organic Molecules for the Prediction of Physical and ADMET Properties. Part 2. The Universal Solvation Equation

Nikolai S. Zefirov, Polina V. Oliferenko, Alexander A. Oliferenko, 2009, J. Chem. Inf. Model..

MFTA: recent extensions of molecular field topology analysis

Vladimir A. Palyulin, Eugene V. Radchenko, O. D. Baranova, 2003 .

Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information

Igor V. Tetko, Antony J. Williams, Artem Cherkasov, 2011, J. Cheminformatics.

A Neural Device for Searching Direct Correlations between Structures and Properties of Chemical Compounds

Igor I. Baskin, Nikolai S. Zefirov, Vladimir A. Palyulin, 1997, J. Chem. Inf. Comput. Sci..

Virtual screening workflow for glycogen synthase kinase 3β inhibitors: convergence of ligand-based and structure-based approaches

Nikolai S. Zefirov, Vladimir A. Palyulin, Dmitry I. Osolodkin, 2011, J. Cheminformatics.

Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information

Igor V. Tetko, Antony J. Williams, Artem Cherkasov, 2011, J. Comput. Aided Mol. Des..

Analysis and visualization of antiviral chemical space

Vladimir A. Palyulin, Dmitry I. Osolodkin, Anastasia A. Nikitina, 2016 .

Computational tool for multi-target drug design

Vladimir A. Palyulin, Eugene V. Radchenko, N. S. Zefirov, 2015 .

Progress in visual representations of chemical space

Nikolai S. Zefirov, Vladimir A. Palyulin, Eugene V. Radchenko, 2015, Expert opinion on drug discovery.

Molecular Field Topology Analysis in drug design and virtual screening

Vladimir A. Palyulin, Eugene V. Radchenko, N. S. Zefirov, 2008 .

QSAR for Boiling Points of "Small" Sulfides. Are the "High-Quality Structure-Property-Activity Regressions" the Real High Quality QSAR Models?

Nikolai S. Zefirov, Vladimir A. Palyulin, 2001, J. Chem. Inf. Comput. Sci..

Building QSAR for HTS in vitro assays - a study for the prediction of Aryl hydrocarbon receptor activators

Igor V. Tetko, Ahmed Abdelaziz, Vladimir A. Palyulin, 2013, Journal of Cheminformatics.

Molecular field topology analysis (MFTA) as the advanced tool for lead optimization

Vladimir A. Palyulin, Eugene V. Radchenko, N. S. Zefirov, 2014 .

Analysis of Chemical Spaces: Implications for Drug Repurposing

Vladimir A. Palyulin, Eugene V. Radchenko, Dmitry I. Osolodkin, 2019, In Silico Drug Design.

Molecular Field Topology Analysis Method in QSAR Studies of Organic Compounds

Nikolai S. Zefirov, Vladimir A. Palyulin, Eugene V. Radchenko, 2000, J. Chem. Inf. Comput. Sci..

RICON-The Computer Program for the Quantitative Investigations of Cyclic Organic Molecule Conformations

Nikolai S. Zefirov, Vladimir A. Palyulin, Alexander Yu. Zotov, 1997, J. Chem. Inf. Comput. Sci..

Kirchhoff atomic charges fitted to multipole moments: Implementation for a virtual screening system

Volodymyr G. Bdzhola, Nikolai S. Zefirov, Alexander A. Oliferenko, 2008, J. Comput. Chem..

Time-Domain Analysis of Molecular Dynamics Trajectories Using Deep Neural Networks: Application to Activity Ranking of Tankyrase Inhibitors

Pradeep Kumar, Viness Pillay, Ahmed Kamal, 2019, J. Chem. Inf. Model..

Computational studies of flaviviruses: approaching to novel fusion inhibitors

Nikolai S. Zefirov, Evgenia V. Dueva, Vladimir A. Palyulin, 2012, Journal of Cheminformatics.

SYMBEQ Program and Its Application in Computer-Assisted Reaction Design

Igor I. Baskin, Nikolai S. Zefirov, Vladimir A. Palyulin, 1994, J. Chem. Inf. Comput. Sci..

Generation of molecular graphs for QSAR studies: an approach based on acyclic fragment combinations

Nikolai S. Zefirov, Vladimir A. Palyulin, Serge S. Tratch, 1992, J. Chem. Inf. Comput. Sci..

Fragmental Approach in QSPR

Nikolai S. Zefirov, Vladimir A. Palyulin, N. Zefirov, 2002, J. Chem. Inf. Comput. Sci..

A new software for fragment-based QSAR and its applications

Nikolai S. Zefirov, Vladimir A. Palyulin, Sergey B. Sosnin, 2014, Journal of Cheminformatics.

Igor I. Baskin, Nikolai S. Zefirov, Vladimir A. Palyulin, 1998, J. Chem. Inf. Comput. Sci..

Generation of Molecular Graphs for QSAR Studies: An Approach Based on Supergraphs

Nikolai S. Zefirov, Andrey Melnikov, Vladimir A. Palyulin, 2007, J. Chem. Inf. Model..

Virtual Computational Chemistry Laboratory – Design and Description

Igor V. Tetko, Johann Gasteiger, Roberto Todeschini, 2005, J. Comput. Aided Mol. Des..

Enhanced taxonomy annotation of antiviral activity data from ChEMBL

Vladimir A. Palyulin, Dmitry I. Osolodkin, Anastasia A. Nikitina, 2019, Database J. Biol. Databases Curation.

Theoretical Scales of Hydrogen Bond Acidity and Basicity for Application in QSAR/QSPR Studies and Drug Design. Partitioning of Aliphatic Compounds

Nikolai S. Zefirov, Robin D. Rogers, Polina V. Oliferenko, 2004, J. Chem. Inf. Model..

Non-linear molecular field topology analysis by means of artificial neural networks

Vladimir A. Palyulin, Eugene V. Radchenko, O. D. Baranova, 2003 .

Influence of molecular descriptor calculation schemes on multiparameter optimization based virtual screening

Vladimir A. Palyulin, Eugene V. Radchenko, Dmitry I. Osolodkin, 2016 .

Chemical graphs and their basis invariants

Igor I. Baskin, L. A. Skvortsov, Vladimir A. Palyulin, 1999 .

Computer-aided design of selective ligands of the benzodiazepine-binding site of the GABAA receptor

Vladimir A. Palyulin, Eugene V. Radchenko, Nikolai S. Zefirov, 2008 .

Quantitative structure–conditions–property relationship studies. Neural network modelling of the acid hydrolysis of esters

Igor I. Baskin, Vladimir A. Palyulin, Nikolai S. Zefirov, 2002 .

Method of continuous molecular fields in the search for quantitative structure-activity relationships

Igor I. Baskin, Vladimir A. Palyulin, N. I. Zhokhova, 2009 .

Organophosphorus compound esterase profiles as predictors of therapeutic and toxic effects.

Vladimir A. Palyulin, Eugene V. Radchenko, Galina F. Makhaeva, 2013, Chemico-biological interactions.

Multilevel approach to the prediction of properties of organic compounds in the framework of the QSAR/QSPR methodology

Igor I. Baskin, Vladimir A. Palyulin, N. I. Zhokhova, 2009 .

Conformational Switching of 3,7‐Diacyl‐3,7‐diazabicyclo[3.3.1]nonanes by Metal Binding and by Solvent Changes

Hans-Jörg Schneider, Vladimir A. Palyulin, H. Schneider, 1999 .

Artificial neural network and fragmental approach in prediction of physicochemical properties of organic compounds

Igor I. Baskin, Vladimir A. Palyulin, N. Artemenko, 2003 .

Atomic charges via electronegativity equalization: Generalizations and perspectives

Vladimir A. Palyulin, S. A. Pisarev, A. Oliferenko, 2006 .

MOLECULAR FIELD TOPOLOGY ANALYSIS IN STUDIES OF QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS FOR ORGANIC COMPOUNDS

Vladimir A. Palyulin, Eugene V. Radchenko, N. S. Zefirov, 1997 .

APPLICATION OF METHODOLOGY OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTING THE PROPERTIES OF SOPHISTICATED MOLECULAR SYSTEMS : PREDICTION OF THE LONG-WAV E ABSORPTION BAND POSITION FOR SYMMETRIC CYANINE DYES

Igor I. Baskin, Vladimir A. Palyulin, N. M. Halberstam, 1997 .

Neural networks as a method for elucidating structure–property relationships for organic compounds

Igor I. Baskin, Vladimir A. Palyulin, Nikolai S. Zefirov, 2003 .

Additive inductive learning in QSAR/QSPR studies and molecular modeling

Igor I. Baskin, Vladimir A. Palyulin, N. I. Zhokhova, 2009 .

Inverse Structure–Property Relationship Problem for the Case of a Correlation Equation Containing the Hosoya Index

Vladimir A. Palyulin, K. S. Fedyaev, M. I. Skvortsova, 2001 .

INVERSE PROBLEM IN THE STRUCTURE-PROPERTY RELATIONSHIP ANALYSIS FOR THE CASE OF INFORMATION TOPOLOGICAL INDICES

Igor I. Baskin, Vladimir A. Palyulin, M. I. Skvortsova, 1997 .

“Bimolecular” QSPR: Estimation of the solvation free energy of organic molecules in different solvents

Igor I. Baskin, Vladimir A. Palyulin, A. A. Kravtsov, 2007 .

A Study of the Affinity of Dyes for Cellulose Fiber within the Framework of a Fragment Approach in QSPR

Igor I. Baskin, Vladimir A. Palyulin, N. I. Zhokhova, 2005 .

Chapter 5:Molecular Field Topology Analysis in Drug Design and Virtual Screening

Vladimir A. Palyulin, Eugene V. Radchenko, Nikolai S. Zefirov, 2008 .

Method for the Construction of Universal Structure–Property Relationship Models using the Example of Normal Boiling Temperature for a Wide Set of Organic Compounds

Vladimir A. Palyulin, A. A. Ivanova, Nikolai S. Zefirov, 2001 .

Prediction of the preferable mechanism of nucleophilic substitution at saturated carbon atom and prognosis of SN1 rate constants by means of QSPR

Igor I. Baskin, Vladimir A. Palyulin, A. A. Kravtsov, 2011 .

Prediction of Physical Properties of Organic Compounds Using Artificial Neural Networks within the Substructure Approach

Igor I. Baskin, Vladimir A. Palyulin, N. Artemenko, 2001 .

Conformations of eight-membered cyclosiloxanes

Vladimir A. Palyulin, V. Shklover, N. Zefirov, 1981 .

QUANTATIVE CHARACTERISTICS OF NINE-MEMBERED RING CONFORMATIONS

Igor I. Baskin, Vladimir A. Palyulin, N. S. Zefirov, 1995 .

Fragment descriptors in QSPR: Application to magnetic susceptibility calculations

Igor I. Baskin, Vladimir A. Palyulin, N. I. Zhokhova, 2004 .

Virtual screening based on one-class classification

Igor I. Baskin, Vladimir A. Palyulin, Nikolai S. Zefirov, 2011 .

A Neural Device for Searching Direct Correlations Between Structures and Properties of Chemical Compounds.

Igor I. Baskin, Vladimir A. Palyulin, I. Baskin, 2010 .

Fragmental descriptors with labeled atoms and their application in QSAR/QSPR studies

Igor I. Baskin, Vladimir A. Palyulin, N. I. Zhokhova, 2007 .

Conformational Analysis of Bicyclo(3.3.1)nonanes and Their Hetero Analogs

Vladimir A. Palyulin, N. Zefirov, Nikolai S. Zefirov, 2010 .

STEREOCHEMICAL STUDIES. XXXIV. * QUANTITATIVE DESCRIPTION OF RING PUCKERING VIA TORSIONAL ANGLES. THE CASE OF SIX-MEMBERED RINGS

Vladimir A. Palyulin, N. S. Zefirov, Nikolai S. Zefirov, 1990 .

Neural-Network Model of the Lipophilicity of Organic Compounds Based on Fragment Descriptors

Vladimir A. Palyulin, Nikolai S. Zefirov, N. V. Artemenko, 2002 .

Fragmental descriptors in QSPR: flash point calculations

Igor I. Baskin, Vladimir A. Palyulin, N. I. Zhokhova, 2003 .

Fragmental descriptors in (Q)SAR: prediction of the assignment of organic compounds to pharmacological groups using the support vector machine approach

Igor I. Baskin, Vladimir A. Palyulin, N. I. Zhokhova, 2009 .

QSAR for Boiling Points of “Small” Sulfides. Are the “High‐Quality Structure—Property—Activity Regressions” the Real High Quality QSAR Models?

Vladimir A. Palyulin, Nikolai S. Zefirov, 2001 .