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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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Inverse folding for antibody sequence design using deep learning

Authors: Frédéric A. Dreyer; Daniel Cutting; Constantin Schneider; Henry Kenlay; Charlotte M. Deane;

Inverse folding for antibody sequence design using deep learning

Abstract

Model weights of the AbMPNN model (arXiv:2310.19513) presented at the 2023 ICML Workshop on Computational Biology, and csv files with the split between train, test and validation across the SAbDab and ImmuneBuilder datasets. This model is based on ProteinMPNN and can be run using the corresponding code: https://github.com/dauparas/ProteinMPNN.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
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104