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A Deep Learning Approach to Antibiotic Discovery
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“We also benchmarked the XGBoost model against a deep learning model based on message-passing neural networks that have shown excellent performance across a range of molecular property prediction tasks39, but these were substantially outperformed by the XGBoost model (Supplementary Fig. 2).”
From Discovery of senolytics using machine learning (2023) · cites this paper as [39]
In the citing paper
“Our approach led to a significant reduction in experimental screening costs, largely because all models were trained solely on published data and, unlike other recent successes in the field39, there was no need to screen compounds purposely for model training.”
From Discovery of senolytics using machine learning (2023) · cites this paper as [39]
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Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-021-01614-0 · Cited by 3,212
Antibiotic resistance and persistence—implications for human health and treatment perspectives
Huemer, M., Mairpady Shambat, S., Brugger, S. D., & Zinkernagel, A. S. (2020) · The EMBO Reports · doi:10.15252/embr.202051034 · Cited by 850
NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products
Kim, H. W., Wang, M., Leber, C. A., Nothias, L.-F., Reher, R., Kang, K. B., van der Hooft, J. J. J., Dorrestein, P. C., Gerwick, W. H., & Cottrell, G. W. (2021) · Journal of Natural Products · doi:10.1021/acs.jnatprod.1c00399 · Cited by 551
Exposing the Limitations of Molecular Machine Learning with Activity Cliffs
van Tilborg, D., Alenicheva, A., & Grisoni, F. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01073 · Cited by 337
Artificial Intelligence for Drug Discovery: Are We There Yet?
Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236
Metadata
- Authors
- Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., Tran, V. M., Chiappino-Pepe, A., Badran, A. H., Andrews, I. W., Chory, E. J., Church, G. M., Brown, E. D., Jaakkola, T. S., Barzilay, R., & Collins, J. J.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 181
- Issue
- 2
- Pages
- 475-483
- ISSN
- 0092-8674
- Cited by
- 212
- References
- 0
- Language
- en
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