Protein design via deep learning

Summary

Protein design via deep learning is a publication^[1].

Key Facts

• Protein design via deep learning authored Kenta Nakai^[2].
• Protein design via deep learning authored Haipeng Gong^[3].
• Protein design via deep learning's instance of is recorded as publication^[4].
• Protein design via deep learning's copyright license is recorded as Creative Commons Attribution-NonCommercial 4.0 International^[5].
• Protein design via deep learning's page is recorded as bbac102^[6].
• Protein design via deep learning's DOI is recorded as 10.1093/BIB/BBAC102^[7].
• Protein design via deep learning's issue is recorded as 3^[8].
• Protein design via deep learning's volume is recorded as 23^[9].
• Protein design via deep learning's publication date is recorded as +2022-05-01T00:00:00Z^[10].
• Protein design via deep learning's PubMed publication ID is recorded as 35348602^[11].
• Protein design via deep learning's PMC publication ID is recorded as 9116377^[12].
• Protein design via deep learning's published in is recorded as Briefings in Bioinformatics^[13].
• Protein design via deep learning's title is recorded as Protein design via deep learning^[14].
• Protein design via deep learning's author name string is recorded as Wenze Ding^[15].
• Protein design via deep learning's cites work is recorded as Molecular de-novo design through deep reinforcement learning^[16].
• Protein design via deep learning's cites work is recorded as Highly accurate protein structure prediction for the human proteome^[17].
• Protein design via deep learning's cites work is recorded as Accurate prediction of protein structures and interactions using a three-track neural network^[18].
• Protein design via deep learning's cites work is recorded as De novo design of picomolar SARS-CoV-2 miniprotein inhibitors^[19].
• Protein design via deep learning's cites work is recorded as Design of a Novel Globular Protein Fold with Atomic-Level Accuracy^[20].
• Protein design via deep learning's cites work is recorded as Predicting protein structures with a multiplayer online game^[21].
• Protein design via deep learning's cites work is recorded as De novo computational design of retro-aldol enzymes^[22].
• Protein design via deep learning's cites work is recorded as Native protein sequences are close to optimal for their structures^[23].
• Protein design via deep learning's cites work is recorded as Control over overall shape and size in de novo designed proteins^[24].
• Protein design via deep learning's cites work is recorded as A general strategy to construct small molecule biosensors in eukaryotes^[25].
• Protein design via deep learning's cites work is recorded as Design of a hyperstable 60-subunit protein dodecahedron. [corrected]^[26].

Body

Designation and Status

Protein design via deep learning's instance of is recorded as publication^[4].