Kuzma Khrabrov

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

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

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.

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

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

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..