Igor V. Tetko

Computational Theory and Mathematics · 1995–2020

A researcher in the Science corpus: 18 papers; trusts 44 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

Computational Theory and Mathematics — 139 citations into it; #2,608 of 12,182 by trust here, #464 by citations
Materials Chemistry — 92 citations into it; #3,480 of 11,513 by trust here, #5,878 by citations
Molecular Biology — 89 citations into it; #10,406 of 17,773 by trust here, #5,452 by citations
Computational Chemistry & Drug Discovery — 74 citations into it
Spectroscopy — 41 citations into it; #545 of 1,674 by trust here, #318 by citations
Artificial Intelligence — 32 citations into it
Physical and Theoretical Chemistry — 29 citations into it
Model Architectures — 24 citations into it

Whom Igor V. Tetko trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

Who trusts Igor V. Tetko overall

Papers from Igor V. Tetko's perspective

Ordered by how strongly the researchers Igor V. Tetko trusts endorse each paper. Same corpus, a different order for every researcher.

ImageNet classification with deep convolutional neural networks
Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
Gradient-based learning applied to document recognition
Attention is All you Need (Neural Information Processing Systems)
Long Short-Term Memory
Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
ImageNet: A large-scale hierarchical image database
Learning Multiple Layers of Features from Tiny Images
ImageNet Large Scale Visual Recognition Challenge
Dropout: a simple way to prevent neural networks from overfitting

Their papers

Virtual Computational Chemistry Laboratory – Design and Description (2005) — 1,460 citations
The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. (2004) — 1,168 citations
Can we estimate the accuracy of ADME-Tox predictions? (2006) — 239 citations
Estimation of Aqueous Solubility of Chemical Compounds Using E-State Indices (2001) — 334 citations
Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection (2008) — 393 citations
Calculation of molecular lipophilicity: state of the art and comparison of methods on more than 96000 compounds (2009) — 604 citations
State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis (2020) — 372 citations
A renaissance of neural networks in drug discovery (2016) — 213 citations
QSAR without borders (2020) — 546 citations
Gene selection from microarray data for cancer classification - a machine learning approach (2005) — 438 citations
Application of Associative Neural Networks for Prediction of Lipophilicity in ALOGPS 2.1 Program (2002) — 568 citations
Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information (2011) — 610 citations
The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS (2016) — 82 citations
Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds (2015) — 759 citations
How Accurately Can We Predict the Melting Points of Drug-like Compounds? (2014) — 92 citations
The Fusarium graminearum Genome Reveals a Link Between Localized Polymorphism and Pathogen Specialization (2007) — 893 citations
Prediction of n-Octanol/Water Partition Coefficients from PHYSPROP Database Using Artificial Neural Networks and E-State Indices (2001) — 381 citations
Neural network studies, 1. Comparison of overfitting and overtraining (1995)