Ultra high diversity factorizable libraries for efficient therapeutic discovery

Zheng Dai; Sachit D. Saksena; Geraldine Horny; Christine Banholzer; Stefan Ewert; David K. Gifford

doi:10.1101/2022.01.17.476670

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Ultra high diversity factorizable libraries for efficient therapeutic discovery

View ORCID ProfileZheng Dai, View ORCID ProfileSachit D. Saksena, Geraldine Horny, Christine Banholzer, Stefan Ewert, View ORCID ProfileDavid K. Gifford

doi: https://doi.org/10.1101/2022.01.17.476670

Zheng Dai

1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge MA 02139, USA

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Sachit D. Saksena

1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge MA 02139, USA

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Geraldine Horny

2Novartis Institutes for BioMedical Research (NIBR), Basel, Switzerland

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Christine Banholzer

2Novartis Institutes for BioMedical Research (NIBR), Basel, Switzerland

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Stefan Ewert

2Novartis Institutes for BioMedical Research (NIBR), Basel, Switzerland

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David K. Gifford

1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge MA 02139, USA

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For correspondence: gifford@mit.edu

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Posted January 18, 2022.

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Ultra high diversity factorizable libraries for efficient therapeutic discovery

Zheng Dai, Sachit D. Saksena, Geraldine Horny, Christine Banholzer, Stefan Ewert, David K. Gifford

bioRxiv 2022.01.17.476670; doi: https://doi.org/10.1101/2022.01.17.476670

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Ultra high diversity factorizable libraries for efficient therapeutic discovery

Zheng Dai, Sachit D. Saksena, Geraldine Horny, Christine Banholzer, Stefan Ewert, David K. Gifford

bioRxiv 2022.01.17.476670; doi: https://doi.org/10.1101/2022.01.17.476670

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