decagon
importedtherapeutics/decagon
Graph convolutional neural network for multirelational link prediction
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Therapeutics
- Subcategory
- unknown
- License
- MIT(osi)
- Status
- dormant
- Maturity
- deployed
- Organization
- mims-harvard
- Country
- unknown
- Homepage
- snap.stanford.edu/decagon
- Repository
- github.com/mims-harvard/decagon
- Documentation
- unknown
- Tags
- deep-learning · embeddings · graph-convolutional-networks · graph-neural-networks · pharmacology · representation-learning
- Regulatory
- unknown
Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- BindingAffinitygraph-convolutional-networks · graph-neural-networks
Exploring deep learning for predicting the binding affinity between a small molecule (i.e. a drug) and a protein.
- geo-gcngraph-convolutional-networks
The official implementation of the SGCN architecture.
- OpenChemgraph-convolutional-networks
OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research
- EchoGraphsgraph-convolutional-networks
[MICCAI 2022] The official repository of Light-weight spatio-temporal graphs for segmentation and ejection fraction prediction in cardiac ultrasound. Project page:…
- fpembedembeddings
FPembed - Generalized Molecular Fingerprint Embeddings
- icsg3drepresentation-learning
3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning (JCIM 2020)
- api.github.com/repos/mims-harvard/decagonretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2022-11-21, 474 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/30.json→ .entries["decagon"]
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