Mentions
Extended reach
Total likes
Video views
AI synopsis
The overall tone reflects a positive perception of the article's significance, with phrases like 'revolutionary method' and 'game-changer' implying high interest and perceived impact. Technical references, such as enzyme reengineering and neural network models, demonstrate engagement and understanding of the subject, shaping a consensus that this work could advance bioengineering fields.
Most discussions acknowledge the relevance of the research, with some expressing strong support for its potential, indicating general agreement on its importance.
The discussions show high curiosity and enthusiasm, highlighting excitement about innovative approaches like CNN in enzyme engineering.
Participants delve into technical aspects and implications, such as enzyme reengineering methods, reflecting moderate but meaningful engagement.
Multiple comments suggest the publication could significantly influence future research and practical applications, indicating perceived high impact.
AIの応用で酵素をもし自在に設計できるようになると、スマートセルも空想とばかり言えなくなる。 Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural network - PubMed、 https://t.co/WyRa1DwcNS
Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural network https://t.co/qLl9dpO3Cc
RT @bionet_papers: Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neur…
Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural network https://t.co/4iClrm2bug
RT @and_protein: Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural…
RT @and_protein: Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural…
Access novoStoic 2.0 to efficiently design bioconversion pathways considering enzyme transformations, pathway complexity, thermodynamics, and microbial chassis. The tool integrates known and putative steps for optimized…
Access novoStoic 2.0 at httpsnovostoicplatformmoleculemakerorg. Watch the tutorial on computational biosynthesis planning to learn how this platform facilitates pathway design, enzyme selection, and thermodynamic…
AIの応用で酵素をもし自在に設計できるようになると、スマートセルも空想とばかり言えなくなる。 Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural network - PubMed、 https://t.co/WyRa1DwcNS
RT @bionet_papers: Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neur…
Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural network https://t.co/4iClrm2bug
RT @and_protein: Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural…
RT @and_protein: Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural…
Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural network https://t.co/qLl9dpO3Cc