openmedical/registry
← registry

EquiBind

imported

therapeutics/equibind

EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein

Machine-generated from the listed sources and not yet reviewed by a human.

record
Category
Therapeutics
Subcategory
unknown
License
MIT(osi)
Status
maintained
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
drug-discovery · equivariance · geometry · graph-neural-networks · molecules · protein-structure · proteins
Regulatory
unknown
built by · 2

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.

similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

  • equidock_publicdrug-discovery · equivariance · geometry

    EquiDock: geometric deep learning for fast rigid 3D protein-protein docking

  • pypdbprotein-structure · proteins

    A Python API for the RCSB Protein Data Bank (PDB)

  • biopandasdrug-discovery · molecules

    Working with molecular structures in pandas DataFrames

  • datamoldrug-discovery · molecules

    Molecular Processing Made Easy.

  • Deep-Drug-Coderdrug-discovery · molecules

    A tensorflow.keras generative neural network for de novo drug design, first-authored in Nature Machine Intelligence while working at AstraZeneca.

  • boltzdrug-discovery · protein-structure

    Official repository for the Boltz biomolecular interaction models

sources
  1. api.github.com/repos/HannesStark/EquiBind
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2025-02-19, 547 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 →

machine-readable

/v1/entries/10.json→ .entries["equibind"]

Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.