Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features-Reference-Cited by-同舟云学术

Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features

Published:2023-01-22 Issue:1 Volume:15 Page:
ISSN:1942-0862
Container-title:mAbs
language:en
Short-container-title:mAbs

Author:

Harmalkar Ameya¹^ORCID,Rao Roshan²^ORCID,Richard Xie Yuxuan³^ORCID,Honer Jonas⁴,Deisting Wibke⁴,Anlahr Jonas⁴,Hoenig Anja⁴,Czwikla Julia⁴^ORCID,Sienz-Widmann Eva⁴,Rau Doris⁴,Rice Austin J.⁵^ORCID,Riley Timothy P.⁵,Li Danqing⁵,Catterall Hannah B.⁵,Tinberg Christine E.⁶^ORCID,Gray Jeffrey J.¹^ORCID,Wei Kathy Y.⁶^ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA

2. Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA

3. Department of Bioengineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

4. Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany

5. Therapeutic Discovery, Amgen Research, Amgen Inc, Thousand Oaks, CA, USA

6. Therapeutic Discovery, Amgen Research, Amgen Inc, South San Francisco, CA, USA

Funder

AH and JJG

NIH

Publisher

Informa UK Limited

Subject

Immunology,Immunology and Allergy

Link

https://www.tandfonline.com/doi/pdf/10.1080/19420862.2022.2163584