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deepmet

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therapeutics/deepmet

A deep one-class model for the identification of endogenous metabolites

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

deepmet project image
GitHub preview card for jackgisby/deepmet. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Therapeutics
Subcategory
unknown
License
GPL-3.0(osi)
Status
dormant
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
anomaly-detection · cheminformatics · deep-learning · pytorch
Regulatory
unknown
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.

  • aidsorbcheminformatics · pytorch

    Python package for deep learning on porous materials and beyond.

  • KEMPNNcheminformatics · pytorch

    Knowledge-Embedded Message-Passing Neural Networks in Python

  • logD_predictorcheminformatics · pytorch

    Prediction of CHI logD from ¹H/¹³C NMR spectra and molecular fingerprints using ML and deep learning.

  • MolDeTrcheminformatics · pytorch

    Chemistry-informed deep learning (1D Deformable-DETR) for automated ¹H NMR multiplet detection: δ, coupling J, proton count and line width in one forward pass.

  • molecular-VAEcheminformatics · pytorch

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

  • molgencheminformatics · pytorch

    Lightweight toolkit for de novo molecular generation: SMILES & SELFIES tokenizers, CharRNN / MolGPT / VAE models, training, sampling, and MOSES-style metrics.

sources
  1. api.github.com/repos/jackgisby/deepmet
    retrieved 2026-08-25 · via github-api

    Machine-imported from GitHub search. Last push 2024-10-31, 3 stars, license reported as GPL-3.0. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/55.json→ .entries["deepmet"]

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