| Deep learning enables rapid identification of potent DDR1 kinase inhibitors A Zhavoronkov, YA Ivanenkov, A Aliper, MS Veselov, VA Aladinskiy, ... Nature biotechnology 37 (9), 1038-1040, 2019 | 1044 | 2019 |
| Potential 2019-nCoV 3C-like protease inhibitors designed using generative deep learning approaches A Zhavoronkov, V Aladinskiy, A Zhebrak, B Zagribelnyy, V Terentiev, ... | 210 | 2020 |
| 3D molecular representations based on the wave transform for convolutional neural networks D Kuzminykh, D Polykovskiy, A Kadurin, A Zhebrak, I Baskov, S Nikolenko, ... Molecular pharmaceutics 15 (10), 4378-4385, 2018 | 120 | 2018 |
| Potential non-covalent SARS-CoV-2 3C-like protease inhibitors designed using generative deep learning approaches and reviewed by human medicinal chemist in virtual reality A Zhavoronkov, B Zagribelnyy, A Zhebrak, V Aladinskiy, V Terentiev, ... | 47 | 2020 |
| Potential COVID-2019 3C-like protease inhibitors designed using generative deep learning approaches. ChemRxiv A Zhavoronkov, V Aladinskiy, A Zhebrak, B Zagribelnyy, V Terentiev, ... Preprint posted online on February 11, 2020 | 47 | 2020 |
| Molecular generation for desired transcriptome changes with adversarial autoencoders R Shayakhmetov, M Kuznetsov, A Zhebrak, A Kadurin, S Nikolenko, ... Frontiers in Pharmacology 11, 269, 2020 | 35 | 2020 |
| Potential 2019-nCoV 3C-like protease inhibitors designed using generative deep learning approaches DNA double-strand break repair in mammalian cells A Zhavoronkov, VA Aladinskiy, A Zhebrak, B Zagribelnyy, V Terentiev, ... View project Matrix-isolated systems modeling View project 10, 2020 | 9 | 2020 |
| Addendum: Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders R Shayakhmetov, M Kuznetsov, A Zhebrak, A Kadurin, S Nikolenko, ... Frontiers in Pharmacology 11, 1236, 2020 | 3 | 2020 |
| Potential 2019-nCoV 3C-like protease inhibitors designed using generative deelearning approaches A Zhavoronkov, VA Aladinskiy, A Zhebrak, B Zagribelnyy, V Terentiev, ... | 2 | 2020 |
| BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning A Zholus, M Kuznetsov, R Schutski, R Shayakhmetov, D Polykovskiy, ... arXiv preprint arXiv:2406.03686, 2024 | 1 | 2024 |
| Mutual information adversarial autoencoder A Aliper, A Zavoronkovs, A Zhebrak, A Kadurin, D Polykovskiy, ... US Patent 11,403,521, 2022 | 1 | 2022 |
| COSMIC: Molecular Conformation Space Modeling in Internal Coordinates with an Adversarial Framework M Kuznetsov, F Ryabov, R Schutski, R Shayakhmetov, YC Lin, A Aliper, ... Journal of Chemical Information and Modeling 64 (9), 3610-3620, 2024 | | 2024 |
| Mutual information adversarial autoencoder A Aliper, A Zavoronkovs, A Zhebrak, A Kadurin, D Polykovskiy, ... US Patent App. 17/842,247, 2022 | | 2022 |
| Potential 2019-nCoV 3C-like protease inhibitors designed using generative deep learning approaches. ChemRxiv A Zhavoronkov, V Aladinskiy, A Zhebrak, B Zagribelnyy, V Terentiev, ... Preprint. https://doi. org/10.26434/chemrxiv 1182 (9102), v1, 2020 | | 2020 |
| APPLICATION OF ARTIFICIAL INTELLIGENCE TO PREDICT THE ECOTOXICOLOGICAL CHARACTERISTICS OF AVERMECTINS TVA AV, DA Polykovskiy, MD Kuznetsov, A Asadulaev, Y Volkov, A Zholus, ... identity 2, 134-153, 2012 | | 2012 |
| 3D Molecular Representations Based on the Wave Transform for Convolutional Neural Networks R Shayakhmetov, D Kuzminykh, A Zhebrak, I Baskov, D Polykovskiy, ... | | |