| 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 | 1223 | 2019 |
| Molecular sets (MOSES): A benchmarking platform for molecular generation models D Polykovskiy, A Zhebrak, B Sanchez Lengeling, S Golovanov, O Tatanov, ... Frontiers in Pharmacology 11, 1931, 2020 | 728* | 2020 |
| Entangled conditional adversarial autoencoder for de novo drug discovery D Polykovskiy, A Zhebrak, D Vetrov, Y Ivanenkov, V Aladinskiy, ... Molecular pharmaceutics 15 (10), 4398-4405, 2018 | 280 | 2018 |
| Potential 2019-nCoV 3C-like protease inhibitors designed using generative deep learning approaches A Zhavoronkov, V Aladinskiy, A Zhebrak, B Zagribelnyy, V Terentiev, ... | 230 | 2020 |
| AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor F Ren, X Ding, M Zheng, M Korzinkin, X Cai, W Zhu, A Mantsyzov, ... Chemical Science 14 (6), 1443-1452, 2023 | 201 | 2023 |
| 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 | 119 | 2018 |
| Chemistry42: an AI-driven platform for molecular design and optimization YA Ivanenkov, D Polykovskiy, D Bezrukov, B Zagribelnyy, V Aladinskiy, ... Journal of chemical information and modeling 63 (3), 695-701, 2023 | 107 | 2023 |
| A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models F Ren, A Aliper, J Chen, H Zhao, S Rao, C Kuppe, IV Ozerov, M Zhang, ... Nature Biotechnology, 1-13, 2024 | 80 | 2024 |
| MolGrow: A graph normalizing flow for hierarchical molecular generation M Kuznetsov, D Polykovskiy Proceedings of the AAAI Conference on Artificial Intelligence 35 (9), 8226-8234, 2021 | 60 | 2021 |
| Quantum computing for near-term applications in generative chemistry and drug discovery A Pyrkov, A Aliper, D Bezrukov, YC Lin, D Polykovskiy, P Kamya, F Ren, ... Drug Discovery Today 28 (8), 103675, 2023 | 53 | 2023 |
| Prediction of clinical trials outcomes based on target choice and clinical trial design with multi‐modal artificial intelligence A Aliper, R Kudrin, D Polykovskiy, P Kamya, E Tutubalina, S Chen, F Ren, ... Clinical Pharmacology & Therapeutics 114 (5), 972-980, 2023 | 51 | 2023 |
| 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, ... | 48 | 2020 |
| PandaOmics: an AI-driven platform for therapeutic target and biomarker discovery P Kamya, IV Ozerov, FW Pun, K Tretina, T Fokina, S Chen, V Naumov, ... Journal of chemical information and modeling 64 (10), 3961-3969, 2024 | 37 | 2024 |
| 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 | 37 | 2020 |
| nach0: multimodal natural and chemical languages foundation model M Livne, Z Miftahutdinov, E Tutubalina, M Kuznetsov, D Polykovskiy, ... Chemical Science 15 (22), 8380-8389, 2024 | 23 | 2024 |
| Molecular sets (moses): a benchmarking platform for molecular generation models. Front Pharmacol D Polykovskiy, A Zhebrak, B Sanchez-Lengeling, S Golovanov, O Tatanov, ... | 20 | 2020 |
| A prior of a googol gaussians: a tensor ring induced prior for generative models M Kuznetsov, D Polykovskiy, D Vetrov, A Zhebrak Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019 | 20 | 2019 |
| Entangled conditional adversarial autoencoder for drug discovery D Polykovskiy, A Kadurin, AM Aliper, A Zhebrak, A Zavoronkovs US Patent 11,680,063, 2023 | 19 | 2023 |
| Mutual information adversarial autoencoder A Aliper, A Zavoronkovs, A Zhebrak, A Kadurin, D Polykovskiy, ... US Patent 11,403,521, 2022 | 13 | 2022 |
| Extracting invariant features from images using an equivariant autoencoder D Kuzminykh, D Polykovskiy, A Zhebrak Asian Conference on Machine Learning, 438-453, 2018 | 13 | 2018 |
Alexander ZhebrakRelationVerified email at relationrx.com
