Kadurin, Artur

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

Polykovskiy, D., Zhebrak, A., Sanchez-Lengeling, B., Golovanov, S., Tatanov, O., Belyaev, S., Kurbanov, R., Artamonov, A., Aladinskiy, V., Veselov, M., et al. (2020) · Frontiers in Pharmacology

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 849

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

Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., & Zhavoronkov, A. (2017) · Molecular Pharmaceutics

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 587

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

Kadurin, A., Aliper, A., Kazennov, A., Mamoshina, P., Vanhaelen, Q., Khrabrov, K., & Zhavoronkov, A. (2017) · Oncotarget

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 350

Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery

Polykovskiy, D., Zhebrak, A., Vetrov, D., Ivanenkov, Y., Aladinskiy, V., Mamoshina, P., Bozdaganyan, M., Aliper, A., Zhavoronkov, A., & Kadurin, A. (2018) · Molecular Pharmaceutics

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 278