Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Wengong Jin"'
Autor:
Gary Liu, Denise B. Catacutan, Khushi Rathod, Kyle Swanson, Wengong Jin, Jody C. Mohammed, Anush Chiappino-Pepe, Saad A. Syed, Meghan Fragis, Kenneth Rachwalski, Jakob Magolan, Michael G. Surette, Brian K. Coombes, Tommi Jaakkola, Regina Barzilay, James J. Collins, Jonathan M. Stokes
Publikováno v:
Nature Chemical Biology.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ab2d9ec38de25c5f4e87b6caa9fa643d
https://doi.org/10.1038/s41589-023-01349-8
https://doi.org/10.1038/s41589-023-01349-8
Autor:
Brent Koscher, Richard B. Canty, Matthew A. McDonald, Kevin P. Greenman, Charles J. McGill, Camille L. Bilodeau, Wengong Jin, Haoyang Wu, Florence H. Vermeire, Brooke Jin, Travis Hart, Timothy Kulesza, Shih-Cheng Li, Tommi S. Jaakkola, Regina Barzilay, Rafael Gómez-Bombarelli, William H. Green, Klavs F Jensen
A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. Two case studies are demonstrated on dye-like molecules, targeting absorpt
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f38d13176cd6d8d45a50015c8e06887f
https://doi.org/10.26434/chemrxiv-2023-r7b01
https://doi.org/10.26434/chemrxiv-2023-r7b01
Autor:
Camille L. Bilodeau, Tommi S. Jaakkola, Thomas H. Kalantar, Regina Barzilay, Wengong Jin, Jillian A. Emerson, Hongyun Xu, Sukrit Mukhopadhyay, Klavs F. Jensen
Publikováno v:
Reaction Chemistry & Engineering. 7:297-309
While molecular discovery is critical for solving many scientific problems, the time and resource costs of experiments make it intractable to fully explore chemical space. Here, we present a generative modeling framework that proposes novel molecules
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1c87fbc9b3b1bbd4e6d2a2d62c083037
https://doi.org/10.1039/d1re00315a
https://doi.org/10.1039/d1re00315a
Publikováno v:
WIREs Computational Molecular Science. 12
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dfc04e98ebf5af08ccb78c9db24021fc
https://doi.org/10.1002/wcms.1608
https://doi.org/10.1002/wcms.1608
Autor:
Tommi S. Jaakkola, Regina Barzilay, Richard T. Eastman, Zina Itkin, Wengong Jin, Jonathan M. Stokes, Alexey V. Zakharov, James J. Collins
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America
Significance COVID-19 has caused more than 2.5 million deaths worldwide. It is imperative that we develop therapies that can mitigate the effect of the disease. While searching for individual drugs for this purpose has been met with difficulties, syn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b112bf815a72f56566e3033a33be806d
https://doi.org/10.1073/pnas.2105070118
https://doi.org/10.1073/pnas.2105070118
Autor:
Alex X. Lu, Wengong Jin, Regina Barzilay, Samuel Goldman, Caroline Uhler, Tommi S. Jaakkola, Karren Yang
Publikováno v:
CVPR
In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development. Building on the recent success of graph neural networks for learning molecular embeddings and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f0055055b3cc967188a830109d6b56eb
https://doi.org/10.1109/cvpr46437.2021.00662
https://doi.org/10.1109/cvpr46437.2021.00662
Autor:
Kyle Swanson, Lindsey A. Carfrae, Eric D. Brown, Jonathan M. Stokes, Kevin Yang, Anush Chiappino-Pepe, George M. Church, Tommi S. Jaakkola, Zohar Bloom-Ackermann, Victoria M. Tran, Regina Barzilay, Craig R. MacNair, James J. Collins, Shawn French, Andres Cubillos-Ruiz, Nina M. Donghia, Ian W. Andrews, Ahmed H. Badran, Wengong Jin, Emma J. Chory
Publikováno v:
Cell
Prof. Collins via Howard Silver
Prof. Collins via Howard Silver
Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e3644902a137a3f94eaca8153af34f0d
https://europepmc.org/articles/PMC8349178/
https://europepmc.org/articles/PMC8349178/
We seek to develop computational methods to accelerate the design of molecules based on specific chemical properties. In computational terms, this task involves continuous embedding and generation of molecular graphs. Our primary contribution is the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::22107a2b1c33c762dffa59f8d85ddd04
https://doi.org/10.1039/9781788016841-00228
https://doi.org/10.1039/9781788016841-00228
Generative models in molecular design tend to be richly parameterized, data-hungry neural models, as they must create complex structured objects as outputs. Estimating such models from data may be challenging due to the lack of sufficient training da
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e9e04a39d89164b38779a94458ed89f
Autor:
Bilodeau, Camille, Wengong Jin, Hongyun Xu, Emerson, Jillian A., Mukhopadhyay, Sukrit, Kalantar, Thomas H., Jaakkola, Tommi, Barzilay, Regina, Jensen, Klavs F.
Publikováno v:
Reaction Chemistry & Engineering; Feb2022, Vol. 7 Issue 2, p297-309, 13p