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openpom

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therapeutics/openpom

Replication of the Principal Odor Map paper by Brian K. Lee et al. (2023).

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
cheminformatics · deep-learning · olfaction · python · pytorch
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.

  • AromaNexuscheminformatics · olfaction

    Provenance-aware integration for odorant, flavor-molecule, and olfactory-receptor data.

  • molecular-VAEcheminformatics · pytorch

    Implementation of the paper - Automatic chemical design using a data-driven continuous representation of molecules

  • posecheck-fastcheminformatics · pytorch

    High-throughput docking pose validation: symmetry-corrected RMSD and lightweight PoseBusters-style distance/clash filters.

  • smiles-transformercheminformatics · pytorch

    Original implementation of the paper "SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery" by Shion Honda et al.

  • DiffHopp: A Graph Diffusion Model for Novel Drug Design via Scaffold Hopping

  • gflownetpytorch

    A PyTorch implementation of a Generative Flow Network (GFlowNet) proposed by Bengio et al. (2021)

sources
  1. api.github.com/repos/ARY2260/openpom
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2025-10-17, 47 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/3.json→ .entries["openpom"]

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