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Kuzma Khrabrov

Showing results (1-10 of 8) with videos related to

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Journal of Cheminformatics|October 31, 2025
Measuring Chemical LLM robustness to molecular representations: a SMILES variation-based frameworkVeronika Ganeeva, Kuzma Khrabrov, Artur Kadurin, et al.
Molecular Pharmaceutics|July 14, 2017
druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in SilicoArtur Kadurin, Sergey Nikolenko, Kuzma Khrabrov, et al.
Journal of Cheminformatics|April 29, 2025
LAGNet: better electron density prediction for LCAO-based data and drug-like substancesKonstantin Ushenin, Kuzma Khrabrov, Artem Tsypin, et al.
Oncotarget|December 29, 2016
The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncologyArtur Kadurin, Alexander Aliper, Andrey Kazennov, et al.
Communications Chemistry|February 18, 2026
A conformational benchmark for optical property prediction with solvent-aware graph neural networksDenis Potapov, Sergei Rogovoi, Kuzma Khrabrov, et al.
Briefings in Bioinformatics|July 7, 2025
AFToolkit: a framework for molecular modeling of proteins with AlphaFold-derived representationsMaria Sindeeva, Alexander Telepov, Nikita Ivanisenko, et al.
Scientific Reports|September 4, 2024
Doping position estimation for FeRh-based alloysEgor Rumiantsev, Kuzma Khrabrov, Artem Tsypin, et al.
Physical Chemistry Chemical Physics : PCCP|October 24, 2022
nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and datasetKuzma Khrabrov, Ilya Shenbin, Alexander Ryabov, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
Journal of Cheminformatics|October 31, 2025
Measuring Chemical LLM robustness to molecular representations: a SMILES variation-based frameworkVeronika Ganeeva, Kuzma Khrabrov, Artur Kadurin, et al.
Molecular Pharmaceutics|July 14, 2017
druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in SilicoArtur Kadurin, Sergey Nikolenko, Kuzma Khrabrov, et al.
Journal of Cheminformatics|April 29, 2025
LAGNet: better electron density prediction for LCAO-based data and drug-like substancesKonstantin Ushenin, Kuzma Khrabrov, Artem Tsypin, et al.
Oncotarget|December 29, 2016
The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncologyArtur Kadurin, Alexander Aliper, Andrey Kazennov, et al.
Communications Chemistry|February 18, 2026
A conformational benchmark for optical property prediction with solvent-aware graph neural networksDenis Potapov, Sergei Rogovoi, Kuzma Khrabrov, et al.
Briefings in Bioinformatics|July 7, 2025
AFToolkit: a framework for molecular modeling of proteins with AlphaFold-derived representationsMaria Sindeeva, Alexander Telepov, Nikita Ivanisenko, et al.
Scientific Reports|September 4, 2024
Doping position estimation for FeRh-based alloysEgor Rumiantsev, Kuzma Khrabrov, Artem Tsypin, et al.
Physical Chemistry Chemical Physics : PCCP|October 24, 2022
nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and datasetKuzma Khrabrov, Ilya Shenbin, Alexander Ryabov, et al.
Pageof 1