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MolDeTr

imported

therapeutics/moldetr

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.

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

MolDeTr project image
GitHub preview card for smidooo/MolDeTr. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Therapeutics
Subcategory
unknown
License
Apache-2.0(osi)
Status
active
Maturity
deployed
Organization
unknown
Country
unknown
Documentation
unknown
Tags
cheminformatics · chemistry · deep-learning · deformable-detr · multiplet-detection · nmr · nmr-spectroscopy · object-detection · pytorch · scientific-computing · spectroscopy · transformer
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.

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    Demiurge is a Python tool for generating ML input data from simulated ¹H/¹³C NMR spectra or ECFP4 fingerprints. It uses local NMRshiftDB2 prediction and RDKit, producing ML-ready CSVs from SMILES in…

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

  • magnetsteinchemistry · nmr · nmr-spectroscopy · spectroscopy

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  • SpectraFPcheminformatics · chemistry · nmr-spectroscopy

    A package to perform fingerprints from spectroscopy datas.

  • molgencheminformatics · chemistry · pytorch · transformer

    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/smidooo/MolDeTr
    retrieved 2026-08-25 · via github-api

    Machine-imported from GitHub search. Last push 2026-08-25, 3 stars, license reported as Apache-2.0. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/2.json→ .entries["moldetr"]

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