Machine learning modeling of protein-intrinsic features predicts tractability of targeted protein degradation

Wubing Zhang; Shourya S. Roy Burman; Jiaye Chen; Katherine A. Donovan; Yang Cao; Boning Zhang; Zexian Zeng; Yi Zhang; Dian Li; Eric S. Fischer; Collin Tokheim; X. Shirley Liu

doi:10.1101/2021.09.27.462040

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Machine learning modeling of protein-intrinsic features predicts tractability of targeted protein degradation

Wubing Zhang, Shourya S. Roy Burman, Jiaye Chen, Katherine A. Donovan, Yang Cao, Boning Zhang, Zexian Zeng, Yi Zhang, Dian Li, Eric S. Fischer, Collin Tokheim, View ORCID ProfileX. Shirley Liu

doi: https://doi.org/10.1101/2021.09.27.462040

Wubing Zhang

1Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA

2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA

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Shourya S. Roy Burman

3Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA

4Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA

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Jiaye Chen

5Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA

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Katherine A. Donovan

3Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA

4Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA

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Yang Cao

6College of Life Sciences, Sichuan University, Chengdu, China

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Boning Zhang

1Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA

2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA

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Zexian Zeng

1Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA

2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA

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Yi Zhang

1Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA

2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA

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Dian Li

1Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA

2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA

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Eric S. Fischer

3Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA

4Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA

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For correspondence: xsliu@ds.dfci.harvard.edu

Collin Tokheim

1Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA

2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA

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For correspondence: xsliu@ds.dfci.harvard.edu

X. Shirley Liu

1Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA

2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA

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ORCID record for X. Shirley Liu
For correspondence: xsliu@ds.dfci.harvard.edu

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Machine learning modeling of protein-intrinsic features predicts tractability of targeted protein degradation

Wubing Zhang, Shourya S. Roy Burman, Jiaye Chen, Katherine A. Donovan, Yang Cao, Boning Zhang, Zexian Zeng, Yi Zhang, Dian Li, Eric S. Fischer, Collin Tokheim, X. Shirley Liu

bioRxiv 2021.09.27.462040; doi: https://doi.org/10.1101/2021.09.27.462040

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Machine learning modeling of protein-intrinsic features predicts tractability of targeted protein degradation

Wubing Zhang, Shourya S. Roy Burman, Jiaye Chen, Katherine A. Donovan, Yang Cao, Boning Zhang, Zexian Zeng, Yi Zhang, Dian Li, Eric S. Fischer, Collin Tokheim, X. Shirley Liu

bioRxiv 2021.09.27.462040; doi: https://doi.org/10.1101/2021.09.27.462040

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