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

• Main Authors: Rahmad Akbar (Author), Philippe A. Robert (Author), Cédric R. Weber (Author), Michael Widrich (Author), Robert Frank (Author), Milena Pavlović (Author), Lonneke Scheffer (Author), Maria Chernigovskaya (Author), Igor Snapkov (Author), Andrei Slabodkin (Author), Brij Bhushan Mehta (Author), Enkelejda Miho (Author), Fridtjof Lund-Johansen (Author), Jan Terje Andersen (Author), Sepp Hochreiter (Author), Ingrid Hobæk Haff (Author), Günter Klambauer (Author), Geir Kjetil Sandve (Author), Victor Greiff (Author)
• Format: Book
• Published: Taylor & Francis Group, 2022-12-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

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...