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auto-qchem

imported

therapeutics/auto-qchem

Auto-QChem is an automated workflow for the generation and storage of DFT calculations for organic molecules.

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

auto-qchem project image
GitHub preview card for doyle-lab-ucla/auto-qchem. 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
maintained
Maturity
deployed
Organization
doyle-lab-ucla
Country
unknown
Documentation
unknown
Tags
automation · cheminformatics · dft-calculations · feature · machine-learning
Regulatory
unknown
built by · 6

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.

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    Automated Quantum Mechanical Environments (AQME) offers transparent and reproducible workflows available for Jupyter Notebooks and command lines, including: 1) RDKit- and CREST-based conformer…

  • get-chemical-smiles-by-cas-or-nameautomation · cheminformatics

    Get chemical SMILES strings (structures) based on the CAS numbers or the names of the chemicals.

  • robertautomation · cheminformatics

    Automated machine learning protocols that start from CSV databases of descriptors or SMILES and produce publication-quality results in Chemistry studies with only one command line.

  • Free and Equal access of technology to all.

sources
  1. api.github.com/repos/doyle-lab-ucla/auto-qchem
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2026-01-14, 130 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/61.json→ .entries["auto-qchem"]

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