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Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies.
Malbranke, Cyril; Bikard, David; Cocco, Simona; Monasson, Rémi; Tubiana, Jérôme.
Affiliation
  • Malbranke C; Laboratory of Physics of the Ecole Normale Supérieure, PSL Research, CNRS UMR 8023, Sorbonne Université, Université de Paris, Paris, France; Institut Pasteur, Université Paris Cité, CNRS UMR 6047, Synthetic Biology, 75015 Paris, France. Electronic address: cyril.malbranke@phys.ens.fr.
  • Bikard D; Institut Pasteur, Université Paris Cité, CNRS UMR 6047, Synthetic Biology, 75015 Paris, France.
  • Cocco S; Laboratory of Physics of the Ecole Normale Supérieure, PSL Research, CNRS UMR 8023, Sorbonne Université, Université de Paris, Paris, France.
  • Monasson R; Laboratory of Physics of the Ecole Normale Supérieure, PSL Research, CNRS UMR 8023, Sorbonne Université, Université de Paris, Paris, France.
  • Tubiana J; Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel. Electronic address: jertubiana@gmail.com.
Curr Opin Struct Biol ; 80: 102571, 2023 06.
Article in En | MEDLINE | ID: mdl-36947951
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Proteins / Machine Learning Language: En Journal: Curr Opin Struct Biol Journal subject: BIOLOGIA MOLECULAR Year: 2023 Document type: Article Country of publication:
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Proteins / Machine Learning Language: En Journal: Curr Opin Struct Biol Journal subject: BIOLOGIA MOLECULAR Year: 2023 Document type: Article Country of publication: