De novo antibody design with SE(3) diffusion
Dreyer, Frédéric A.; Cutting, Daniel;
De novo antibody design with SE(3) diffusion
Code and model weights of the IgDiff model (arXiv:2405.07622) presented at the 2023 NeurIPS Workshop on New Frontiers of AI for Drug Discovery and Development, as well as a sample of generated structures. This model is based on FrameDiff and the corresponding source code: https://github.com/jasonkyuyim/se3_diffusion.
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).0 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.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
selected citations
Citations provided by BIP!
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).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP! 0
Average
Average
Average