In silico proof of principle of machine learning-based antibody design at unconstrained scale

Generative machine learning (ML) has been postulated to become a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences fo...

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Detalles Bibliográficos
Autores principales: Akbar, Rahmad, Robert, Philippe A., Weber, Cédric R., Widrich, Michael, Frank, Robert, Pavlović, Milena, Scheffer, Lonneke, Chernigovskaya, Maria, Snapkov, Igor, Slabodkin, Andrei, Mehta, Brij Bhushan, Miho, Enkelejda, Lund-Johansen, Fridtjof, Andersen, Jan Terje, Hochreiter, Sepp, Hobæk Haff, Ingrid, Klambauer, Günter, Sandve, Geir Kjetil, Greiff, Victor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2022
Materias:
Report
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8986205/
https://www.ncbi.nlm.nih.gov/pubmed/35377271
http://dx.doi.org/10.1080/19420862.2022.2031482

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8986205/
https://www.ncbi.nlm.nih.gov/pubmed/35377271
http://dx.doi.org/10.1080/19420862.2022.2031482

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