Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction-Reference-Cited by-同舟云学术

Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction

Published:2022-12-19 Issue:12 Volume:2 Page:845-865
ISSN:2662-8457
Container-title:Nature Computational Science
language:en
Short-container-title:Nat Comput Sci

Author:

Robert Philippe A.^ORCID,Akbar Rahmad,Frank Robert^ORCID,Pavlović Milena^ORCID,Widrich Michael^ORCID,Snapkov Igor,Slabodkin Andrei,Chernigovskaya Maria,Scheffer Lonneke^ORCID,Smorodina Eva^ORCID,Rawat Puneet,Mehta Brij Bhushan^ORCID,Vu Mai Ha^ORCID,Mathisen Ingvild Frøberg,Prósz Aurél,Abram Krzysztof,Olar Alex,Miho Enkelejda,Haug Dag Trygve Tryslew,Lund-Johansen Fridtjof,Hochreiter Sepp,Haff Ingrid Hobæk,Klambauer Günter,Sandve Geir Kjetil^ORCID,Greiff Victor^ORCID

Funder

Stiftelsen Kristian Gerhard Jebsen

Norges Forskningsråd

Leona M. and Harry B. Helmsley Charitable Trust

EC | Horizon 2020 Framework Programme

Kreftforeningen

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Computer Science (miscellaneous)

Link

https://www.nature.com/articles/s43588-022-00372-4.pdf