Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Boris, Sattarov"'
Autor:
Artem A. Mitrofanov, Petr I. Matveev, Kristina V. Yakubova, Alexandru Korotcov, Boris Sattarov, Valery Tkachenko, Stepan N. Kalmykov
Publikováno v:
Molecules, Vol 26, Iss 11, p 3237 (2021)
Modern structure–property models are widely used in chemistry; however, in many cases, they are still a kind of a “black box” where there is no clear path from molecule structure to target property. Here we present an example of deep learning u
Externí odkaz:
https://doaj.org/article/581f88bf57644f1d8b98a1054191bcc4
Towered Actor Critic For Handling Multiple Action Types In Reinforcement Learning For Drug Discovery
Autor:
Sai Krishna Gottipati, Yashaswi Pathak, Boris Sattarov, null Sahir, Rohan Nuttall, Mohammad Amini, Matthew E. Taylor, Sarath Chandar
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 35:142-150
Reinforcement learning (RL) has made significant progress in both abstract and real-world domains, but the majority of state-of-the-art algorithms deal only with monotonic actions. However, some applications require agents to reason over different ty
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::16e097656f58c4233dacd3bc2c0832d9
https://doi.org/10.1609/aaai.v35i1.16087
https://doi.org/10.1609/aaai.v35i1.16087
Autor:
Léa El Khoury, Zhifeng Jing, Alberto Cuzzolin, Alessandro Deplano, Daniele Loco, Boris Sattarov, Florent Hédin, Sebastian Wendeborn, Chris Ho, Dina El Ahdab, Theo Jaffrelot Inizan, Mattia Sturlese, Alice Sosic, Martina Volpiana, Angela Lugato, Marco Barone, Barbara Gatto, Maria Ludovica Macchia, Massimo Bellanda, Roberto Battistutta, Cristiano Salata, Ivan Kondratov, Rustam Iminov, Andrii Khairulin, Yaroslav Mykhalonok, Anton Pochepko, Volodymyr Chashka-Ratushnyi, Iaroslava Kos, Stefano Moro, Matthieu Montes, Pengyu Ren, Jay W. Ponder, Louis Lagardère, Jean-Philip Piquemal, Davide Sabbadin
Publikováno v:
Chemical Science
Chemical Science, 2022, ⟨10.1039/D1SC05892D⟩
Chemical Science, 2022, ⟨10.1039/D1SC05892D⟩
We report a fast-track computationally-driven discovery of new SARS-CoV2 Main Protease (Mpro) inhibitors whose potency range from mM for initial non-covalent ligands to high nM for the final covalent compound (IC50=830 +/- 50 nM). The project extensi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7fb3c770ad5b156560e839423dea37d
https://hal.science/hal-03361062
https://hal.science/hal-03361062
Autor:
Esben Jannik Bjerrum, Boris Sattarov
Publikováno v:
Biomolecules, Vol 8, Iss 4, p 131 (2018)
Chemical autoencoders are attractive models as they combine chemical space navigation with possibilities for de novo molecule generation in areas of interest. This enables them to produce focused chemical libraries around a single lead compound for e
Externí odkaz:
https://doaj.org/article/8fa63ac72e6846919cc0f2c2bf8c713a
Autor:
Léa, El Khoury, Zhifeng, Jing, Alberto, Cuzzolin, Alessandro, Deplano, Daniele, Loco, Boris, Sattarov, Florent, Hédin, Sebastian, Wendeborn, Chris, Ho, Dina, El Ahdab, Theo, Jaffrelot Inizan, Mattia, Sturlese, Alice, Sosic, Martina, Volpiana, Angela, Lugato, Marco, Barone, Barbara, Gatto, Maria Ludovica, Macchia, Massimo, Bellanda, Roberto, Battistutta, Cristiano, Salata, Ivan, Kondratov, Rustam, Iminov, Andrii, Khairulin, Yaroslav, Mykhalonok, Anton, Pochepko, Volodymyr, Chashka-Ratushnyi, Iaroslava, Kos, Stefano, Moro, Matthieu, Montes, Pengyu, Ren, Jay W, Ponder, Louis, Lagardère, Jean-Philip, Piquemal, Davide, Sabbadin
Publikováno v:
Chemical science. 13(13)
We report a fast-track computationally driven discovery of new SARS-CoV-2 main protease (M
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::09aa3a22bcf963a31ded78c4b6b5bf9a
https://pubmed.ncbi.nlm.nih.gov/35432906
https://pubmed.ncbi.nlm.nih.gov/35432906
Publikováno v:
Molecular pharmaceutics. 16(10)
Chemical space is impractically large, and conventional structure-based virtual screening techniques cannot be used to simply search through the entire space to discover effective bioactive molecules. To address this shortcoming, we propose a generat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bbe04ab8ea2e05b6e13c7a25a2293015
https://pubmed.ncbi.nlm.nih.gov/31437001
https://pubmed.ncbi.nlm.nih.gov/31437001
Autor:
Esben Jannik Bjerrum, Dragos Horvath, Alexandre Varnek, Gilles Marcou, Boris Sattarov, Igor I. Baskin
Publikováno v:
Journal of Chemical Information and Modeling
Journal of Chemical Information and Modeling, American Chemical Society, 2018, 59 (3), pp.1182-1196. ⟨10.1021/acs.jcim.8b00751⟩
Journal of Chemical Information and Modeling, American Chemical Society, 2018, 59 (3), pp.1182-1196. ⟨10.1021/acs.jcim.8b00751⟩
International audience; Here we show that Generative Topographic Mapping (GTM) can be used to explore the latent space of the SMILES-based autoencoders and generate focused molecular libraries of interest. We have built a sequence-to-sequence neural
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55a5870ef25e3592839a8584d055234e
https://hal.archives-ouvertes.fr/hal-02346951
https://hal.archives-ouvertes.fr/hal-02346951
Autor:
Boris Sattarov, Esben Jannik Bjerrum
Publikováno v:
Biomolecules, Vol 8, Iss 4, p 131 (2018)
Biomolecules
Volume 8
Issue 4
Biomolecules
Volume 8
Issue 4
Chemical autoencoders are attractive models as they combine chemical space navigation with possibilities for de novo molecule generation in areas of interest. This enables them to produce focused chemical libraries around a single lead compound for e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70ddf3982df024d66062b48105762cef
https://doi.org/10.3390/biom8040131
https://doi.org/10.3390/biom8040131