Artur Kadurin

发表

nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset.

P. Zhilyaev, P. Strashnov, A. Ryabov, 2022, Physical chemistry chemical physics : PCCP.

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

Alán Aspuru-Guzik, Sergey I. Nikolenko, Alex Zhavoronkov, 2018, Frontiers in Pharmacology.

druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico.

Sergey Nikolenko, Alex Aliper, Alex Zhavoronkov, 2017, Molecular pharmaceutics.

3D Molecular Representations Based on the Wave Transform for Convolutional Neural Networks.

Rim Shayakhmetov, Alexander Zhebrak, Sergey Nikolenko, 2018, Molecular pharmaceutics.

The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology

Andrey Kazennov, Artur Kadurin, Alexander Aliper, 2016, Oncotarget.

Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders

Maksim Kuznetsov, Rim Shayakhmetov, Alexander Zhebrak, 2020, Frontiers in Pharmacology.

Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery.

Dmitry Vetrov, Alexander Zhebrak, Alexander Aliper, 2018, Molecular pharmaceutics.

Drug and Disease Interpretation Learning with Biomedical Entity Representation Transformer

Elena Tutubalina, Zulfat Miftahutdinov, Roman Kudrin, 2021, ECIR.

Medical concept normalization in clinical trials with drug and disease representation learning

Elena Tutubalina, Zulfat Miftahutdinov, Roman Kudrin, 2021, Bioinform..

Fair Evaluation in Concept Normalization: a Large-scale Comparative Analysis for BERT-based Models

Elena Tutubalina, Zulfat Miftahutdinov, Artur Kadurin, 2020, COLING.

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

Alán Aspuru-Guzik, A. Zhavoronkov, S. Nikolenko, 2018, Frontiers in Pharmacology.

Gradual Optimization Learning for Conformational Energy Minimization

Manvel Avetisian, Alexey Skrynnik, Kuzma Khrabrov, 2023, ICLR.

∇2DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials

Anton M. Alekseev, Ilya Shenbin, Kuzma Khrabrov, 2024, ArXiv.

FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough Reproduction

Manvel Avetisian, Kuzma Khrabrov, Artem Tsypin, 2024, Trans. Mach. Learn. Res..