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SynFrag

imported

therapeutics/synfrag

Synthetic Accessibility via Fragment Assembly Generation

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

SynFrag project image
GitHub preview card for simmzx/SynFrag. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Therapeutics
Subcategory
unknown
License
MIT(osi)
Status
active
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
ai4science · aidd · cheminformatics · deep-learning · molecule-modeling · synthetic-accessibility
Regulatory
unknown
built by · 1

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.

  • BBARsynthetic-accessibility

    Official Github for "Molecular generative model via retrosynthetically prepared chemical building block assembly" (Advanced Science)

  • This project uses a Variational Autoencoder (VAE) to generate SMILES strings for novel compound generation. The VAE model is trained on a dataset of existing chemical compounds and can generate new,…

  • aidsorbai4science · cheminformatics

    Python package for deep learning on porous materials and beyond.

  • molgenai4science · cheminformatics

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

  • ChemFlowai4science

    Uncover meaningful structures of latent spaces learned by generative models with flows!

  • cMolGPTai4science

    GPT (Generative Pre-trained Transformer) for de novo molecular design by enforcing specified targets

sources
  1. api.github.com/repos/simmzx/SynFrag
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2026-07-03, 24 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.

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machine-readable

/v1/entries/45.json→ .entries["synfrag"]

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