Igor V. Tetko

Igor V. Tetko, V. Tanchuk, 2003, Second IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2003. Proceedings.

Neural Network Studies. 3. Variable Selection in the Cascade-Correlation Learning Architecture

Igor V. Tetko, Alessandro E. P. Villa, David J. Livingstone, 1998, J. Chem. Inf. Comput. Sci..

Internet Software for the Calculation of the Lipophilicity and Aqueous Solubility of Chemical Compounds

Igor V. Tetko, Alessandro E. P. Villa, Vsevolod Yu. Tanchuk, 2001, J. Chem. Inf. Comput. Sci..

Associative Neural Network

Igor V. Tetko, I. Tetko, 2002, Neural Processing Letters.

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.

Application of a Pruning Algorithm To Optimize Artificial Neural Networks for Pharmaceutical Fingerprinting

Igor V. Tetko, William J. Welsh, Alessandro E. P. Villa, 1998, J. Chem. Inf. Comput. Sci..

Chemogenomic approach to increase accuracy of QSAR modeling of inhibition activity against five major P450 isoforms

Igor V. Tetko, Iurii Sushko, Sergii Novotarskyi, 2013, Journal of Cheminformatics.

GEN: highly efficient SMILES explorer using autodidactic generative examination networks pdf

Igor V. Tetko, Guillaume Godin, Ruud van Deursen, 2019, Journal of Cheminformatics.

Exploiting gene interaction information from microarray expression data for cancer classification

Igor V. Tetko, Alexey V. Antonov, Michael T. Mader, 2003, German Conference on Bioinformatics.

Spatiotemporal Expression Control Correlates with Intragenic Scaffold Matrix Attachment Regions (S/MARs) in Arabidopsis thaliana

Igor V. Tetko, Georg Haberer, Hans-Werner Mewes, 2006, PLoS Comput. Biol..

QSAR modeling for In vitro assays: linking ToxCast™ database to the integrated modeling framework “OCHEM”

Igor V. Tetko, Ahmed Abdelaziz, Iurii Sushko, 2012, Journal of Cheminformatics.

Modeling the Biodegradability of Chemical Compounds Using the Online CHEmical Modeling Environment (OCHEM)

Igor V. Tetko, Susann Vorberg, I. Tetko, 2013, Molecular informatics.

Focused Library Generator: case of Mdmx inhibitors

Igor V. Tetko, Pavel Karpov, Zhonghua Xia, 2019, Journal of Computer-Aided Molecular Design.

Calculation of molecular lipophilicity: state of the art and comparison of methods on more than 96000 compounds

Igor V. Tetko, M Mannhold, Gennady Poda, 2009 .

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..

Exploiting scale-free information from expression data for cancer classification

Igor V. Tetko, Alexey V. Antonov, Hans-Werner Mewes, 2005, German Conference on Bioinformatics.

Synergy Effect between Convolutional Neural Networks and the Multiplicity of SMILES for Improvement of Molecular Prediction pdf

Igor V. Tetko, Eric Bruno, Talia B. Kimber, 2018, ArXiv.

Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis

Igor V. Tetko, Hao Zhu, Artem Cherkasov, 2008, J. Chem. Inf. Model..

Recognition of Neurons Impulses with the Use of Nonlinear Dynamic Equations

Igor V. Tetko, Olga K. Chibirova, Tatyana I. Aksenova, 2001 .

OCHEM - on-line CHEmical database & modeling environment

Igor V. Tetko, Matthias Rupp, Anil Kumar Pandey, 2010, J. Cheminformatics.

An Efficient Partition of Training Data Set Improves Speed and Accuracy of Cascade-correlation Algorithm

Igor V. Tetko, Alessandro E. P. Villa, 2004, Neural Processing Letters.

Gene selection from microarray data for cancer classification - a machine learning approach

Igor V. Tetko, Eibe Frank, Mark A. Hall, 2005, Comput. Biol. Chem..

Multitask Learning On Graph Neural Networks Applied To Molecular Property Predictions pdf

Igor V. Tetko, Guillaume Godin, Ruud Van Deursen, 2019, ArXiv.

Applicability domain for classification problems

Igor V. Tetko, Anil Kumar Pandey, Iurii Sushko, 2010, J. Cheminformatics.

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.

Correction: Spatiotemporal Expression Control Correlates with Intragenic Scaffold Matrix Attachment Regions (S/MARs) in Arabidopsis thaliana

Igor V. Tetko, Georg Haberer, Hans-Werner Mewes, 2006, PLoS Comput. Biol..

Prediction of n-Octanol/Water Partition Coefficients from PHYSPROP Database Using Artificial Neural Networks and E-State Indices

Igor V. Tetko, Alessandro E. P. Villa, Vsevolod Yu. Tanchuk, 2001, J. Chem. Inf. Comput. Sci..

BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btm633 Sequence analysis

Igor V. Tetko, Thomas Rattei, Hans-Werner Mewes, 2008 .

Anti AIDS drug design with the help of neural networks

Igor V. Tetko, V. Yu. Tanchuk, A. I. Luik, 2008 .

Application of Self-Organizing Neural Networks with Active Neurons for QSAR Studies

Igor V. Tetko, A. I. Luik, David J. Livingstone, 2000 .

Detection of deterministic dynamics in short discrete time series.

Igor V. Tetko, Alessandro E. P. Villa, Alessandra Celletti, 2000 .

Pattern grouping algorithm and de-convolution filtering of non-stationary correlated Poisson processes

Igor V. Tetko, Alessandro E. P. Villa, 2001, Neurocomputing.

Accurate In Silico log P Predictions: One Can't Embrace the Unembraceable

Igor V. Tetko, Raimund Mannhold, Claude Ostermann, 2009 .

Consensus Modeling for HTS Assays Using In silico Descriptors Calculates the Best Balanced Accuracy in Tox21 Challenge

Igor V. Tetko, Karl-Werner Schramm, Ahmed Abdelaziz, 2016, Front. Environ. Sci..

Analysis of the deterministic behaviour of experimental series

Igor V. Tetko, Alessandra Celletti, A. E. P. Villa, 1998 .

The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS

Igor V. Tetko, Antony J. Williams, Daniel M. Lowe, 2016, Journal of Cheminformatics.

Determination of chaotic attractors in short discrete time series

Igor V. Tetko, Alessandro E. P. Villa, Alessandra Celletti, 2001 .

Computer assisted neurophysiology by a distributed JAVA program

Igor V. Tetko, Luc Jeandenans, Michel Gautero, 1998, HCC.

ToxAlerts: A Web Server of Structural Alerts for Toxic Chemicals and Compounds with Potential Adverse Reactions

Igor V. Tetko, Iurii Sushko, Vladimir Potemkin, 2012, J. Chem. Inf. Model..

Neural Network Studies, 2. Variable Selection

Igor V. Tetko, Alessandro E. P. Villa, David J. Livingstone, 1996, J. Chem. Inf. Comput. Sci..

Troubles with Chemoinformatics and Associative Neural Networks

Igor V. Tetko, I. Tetko, 2006 .

Exhaustive QSPR Studies of a Large Diverse Set of Ionic Liquids: How Accurately Can We Predict Melting Points?

Igor V. Tetko, Igor I. Baskin, Alexandre Varnek, 2007, J. Chem. Inf. Model..

Surrogate data – a secure way to share corporate data

Igor V. Tetko, Tudor I. Oprea, Ruben Abagyan, 2005, J. Comput. Aided Mol. Des..

A Transformer Model for Retrosynthesis

Igor V. Tetko, Pavel Karpov, Guillaume Godin, 2019, ICANN.

Development of Dimethyl Sulfoxide Solubility Models Using 163 000 Molecules: Using a Domain Applicability Metric to Select More Reliable Predictions

Igor V. Tetko, Vladimir Ivanov, Benoît Mathieu, 2013, J. Chem. Inf. Model..

Support vector machines for separation of mixed plant?Cpathogen EST collections based on codon usage

Igor V. Tetko, Caroline C. Friedel, Katharina Jahn, 2005, Bioinform..

Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Set

Igor V. Tetko, Artem Cherkasov, Klaus-Robert Müller, 2010, J. Chem. Inf. Model..

Spatiotemporal activity patterns detected from single cell measurements from behaving animals

Igor V. Tetko, Alessandro E. P. Villa, I. Tetko, 1999, Other Conferences.

PLS-Optimal: A Stepwise D-Optimal Design Based on Latent Variables

Igor V. Tetko, Ullrika Sahlin, Tomas Öberg, 2012, J. Chem. Inf. Model..

Inductive Transfer of Knowledge: Application of Multi-Task Learning and Feature Net Approaches to Model Tissue-Air Partition Coefficients

Igor V. Tetko, Igor I. Baskin, Gilles Marcou, 2009, J. Chem. Inf. Model..

Application of Self-Organizing Neural Networks with Active Neurons for Prediction of Bioactivity of Chemical Compounds by the Analogues Search Algorithm

Igor V. Tetko, A. I. Luik, Alexey G. Ivakhnenko, 1999 .

MIPS bacterial genomes functional annotation benchmark dataset

Igor V. Tetko, Alexey V. Antonov, Hans-Werner Mewes, 2005, Bioinform..

In silico pKa prediction

Igor V. Tetko, Iurii Sushko, Sergii Novotarskyi, 2012, Journal of Cheminformatics.

Data modelling with neural networks: Advantages and limitations

Igor V. Tetko, David J. Livingstone, David T. Manallack, 1997, J. Comput. Aided Mol. Des..

Input Parameters' estimation via neural networks

Igor V. Tetko, Alexander I. Luik, 1994, ESANN.

Internet in Drug Design and Discovery

Igor V. Tetko, I. Tetko, 2008 .

Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection

Igor V. Tetko, Hao Zhu, Alexandre Varnek, 2008, J. Chem. Inf. Model..

Neural Network Studies, 4. Introduction to Associative Neural Networks

Igor V. Tetko, I. Tetko, 2002, J. Chem. Inf. Comput. Sci..

A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 2. Application to simultaneous single unit recordings

Igor V. Tetko, Alessandro E. P. Villa, I. Tetko, 2001, Journal of Neuroscience Methods.

Neural network studies, 1. Comparison of overfitting and overtraining

Igor V. Tetko, David J. Livingstone, Alexander I. Luik, 1995, J. Chem. Inf. Comput. Sci..

A COOPERATIVE COMPUTING APPROACH TO DECIPHER NEURAL CODING

Igor V. Tetko, Javier Iglesias, 2001 .

Transformer-CNN: Fast and Reliable tool for QSAR pdf

Igor V. Tetko, Guillaume Godin, Pavel Karpov, 2019, ArXiv.

Deterministic dynamics of discrete time series: a review of the basic techniques and a recent method for short time series

Igor V. Tetko, Alessandra Celletti, Claude Froeschlé, 2001 .

Variable Selection in the Cascade-Correlation Learning Architecture

Igor V. Tetko, A. I. Luik, David J. Livingstone, 2000 .

Virtual Computational Chemistry Laboratory – Design and Description

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

Benchmarking of Linear and Nonlinear Approaches for Quantitative Structure-Property Relationship Studies of Metal Complexation with Ionophores

Igor V. Tetko, Nicolas Lachiche, Frank Hoonakker, 2006, J. Chem. Inf. Model..

Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization process

Igor V. Tetko, Ahmed Abdelaziz, Iurii Sushko, 2014, Journal of Cheminformatics.

Éclair — a web service for unravelling species origin of sequences sampled from mixed host interfaces

Igor V. Tetko, Stephen Rudd, I. Tetko, 2005 .

Classification of CYP450 1A2 inhibitors using PubChem data

Igor V. Tetko, Anil Kumar Pandey, Iurii Sushko, 2010, J. Cheminformatics.

An Enhancement of Generalization Ability in Cascade Correlation Algorithm by Avoidance of Overfitting/Overtraining Problem

Igor V. Tetko, Alessandro E. P. Villa, 2004, Neural Processing Letters.

Transformer-CNN: Swiss knife for QSAR modeling and interpretation

Igor V. Tetko, Guillaume Godin, Pavel Karpov, 2020, Journal of Cheminformatics.

A web portal for classification of expression data using maximal margin linear programming

Igor V. Tetko, Alexey V. Antonov, Hans-Werner Mewes, 2004, Bioinform..

Estimation of Aqueous Solubility of Chemical Compounds Using E-State Indices

Igor V. Tetko, Alessandro E. P. Villa, Vsevolod Yu. Tanchuk, 2001, J. Chem. Inf. Comput. Sci..

Modeling of non-additive mixture properties using the Online CHEmical database and Modeling environment (OCHEM)

Igor V. Tetko, Ioana Oprisiu, Sergii Novotarskyi, 2013, Journal of Cheminformatics.

FunCat functional inference with belief propagation and feature integration

Igor V. Tetko, Hans-Werner Mewes, Dimitrij Surmeli, 2008, Comput. Biol. Chem..

Environmental Risk Assessment using the QSPR-Thesaurus Web tool

Igor V. Tetko, Willie J.G.M. Peijnenburg, Pantelis Sopasakis, 2013 .

Beware of proper validation of models for ionic Liquids!

Igor V. Tetko, Dmitriy M. Makarov, Yu. A. Fadeeva, 2021, Journal of Molecular Liquids.

ehp CERAPP : Collaborative Estrogen Receptor Activity Prediction Project

Ruili Huang, Igor V. Tetko, Eva Bay Wedebye, 2016 .

Prediction of Log P with Property‐Based Methods

Igor V. Tetko, Gennadiy I. Poda, G. Poda, 2007 .

BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry

Igor V. Tetko, Uwe Koch, Ola Engkvist, 2016, Molecular informatics.

Identification of Small-Molecule Frequent Hitters from AlphaScreen High-Throughput Screens

Igor V. Tetko, Jeanette Reinshagen, Kenji Schorpp, 2014, Journal of biomolecular screening.

The Good, the Bad and the Ugly of Distribution Coefficients: Current Status, Views and Outlook

Igor V. Tetko, Franco Lombardo, Bernard Faller, 2007 .

Lattice Model for QSAR Studies

Igor V. Tetko, Anatoly G. Artemenko, David J. Livingstone, 2000 .

Comparison of Overfitting and Overtraining

Igor V. Tetko, David J. Livingstone, Alexander I. Luik, 2001 .

CADASTER QSPR Models for Predictions of Melting and Boiling Points of Perfluorinated Chemicals

Igor V. Tetko, Paola Gramatica, Tomas Öberg, 2011, Molecular informatics.

ToxCast EPA in Vitro to in Vivo Challenge: Insight into the Rank-I Model

Igor V. Tetko, Sergii Novotarskyi, Robert Körner, 2016, Chemical research in toxicology.

CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

Ruili Huang, Igor V. Tetko, Eva Bay Wedebye, 2016, Environmental health perspectives.

Electronic‐Topological Investigation of theStructure – Acetylcholinesterase Inhibitor Activity Relationship in the Series of N‐Benzylpiperidine Derivatives

Igor V. Tetko, David J. Livingstone, A. S. Dimoglo, 2001 .

Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis

Igor V. Tetko, Artem Cherkasov, Paola Gramatica, 2008, Journal of Chemical Information and Modeling.

Robust polynomial neural networks in quantative-structure activity relationship studies

Igor V. Tetko, Vladimir V. Volkovich, I. Tetko, 2003 .

Joint Virtual Special Issue on Computational Toxicology

Igor V. Tetko, Alexander Tropsha, Igor V Tetko, 2020, J. Chem. Inf. Model..

Deterministic Behaviour of Short Time Series

Igor V. Tetko, Alessandro E. P. Villa, Alessandra Celletti, 1999 .

A systematic approach to infer biological relevance and biases of gene network structures

Igor V. Tetko, H. Mewes, I. Tetko, 2006, Nucleic acids research.

Rule-Based Systems to Predict Lipophilicity

Igor V. Tetko, David J. Livingstone, I. Tetko, 2007 .

Experimental and Theoretical Studies in the EU FP7 Marie Curie Initial Training Network Project, Environmental ChemOinformatics (ECO)

Igor V. Tetko, Roberto Todeschini, Willie J.G.M. Peijnenburg, 2014, Alternatives to laboratory animals : ATLA.

The Prediction of Physicochemical Properties

Igor V. Tetko, I. Tetko, 2006 .