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Open Source Science

https://github.com/choderalab/gimlet

Graph Inference on MoLEcular Topology

https://github.com/choderalab/gimlet

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    2 of 7 committers (28.6%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (7.8%) to scientific vocabulary

Keywords

graph-theory molecular-modeling tensorflow-gpu
Last synced: 9 months ago · JSON representation

Repository

Graph Inference on MoLEcular Topology

Basic Info
  • Host: GitHub
  • Owner: choderalab
  • License: other
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 27.4 MB
Statistics
  • Stars: 26
  • Watchers: 10
  • Forks: 9
  • Open Issues: 3
  • Releases: 0
Topics
graph-theory molecular-modeling tensorflow-gpu
Created about 7 years ago · Last pushed about 3 years ago
Metadata Files
Readme License

README.md

gimlet

Graph Inference on MoLEcular Topology. A package for modelling, learning, and inference on molecular topological space written in Python and TensorFlow.

Dependencies

gimlet doesn't depend on any packages except TensorFlow 2.0, and pandas and pytest, if you must.

Examples

https://github.com/choderalab/gimlet/blob/master/lime/scripts/notebooks/190728yuanqinggnwithgruonsereinarinikerdataset.ipynb

Authors

  • yuanqing wang <yuanqing.wang@choderalab.org><wangyq@wangyq.net> (while at Chodera Lab at Memorial Sloan Kettering Cancer Center, Weill Cornell Medicine, and the City College of the City University of New York.)

Manifest

  • gin/ the core (and fun) part of the package.
    • i_o/ reading and writing popular molecule embedding/representing structures.
    • deterministic/ property predictions, conformer and charge generations.
    • probabilistic/ molecular machine learning through graph networks.
  • lime/ auxiliary scripts.
    • for_biologists/ ready-to-use modules and scripts.
    • architectures/ off-the-shelf model architectures developed elsewhere.
    • scripts/ fun scripts we used to generate data and hypothesis.
    • trained_models/ Nomen est omen.

Owner

  • Name: Chodera lab // Memorial Sloan Kettering Cancer Center
  • Login: choderalab
  • Kind: organization
  • Email: john.chodera@choderalab.org
  • Location: Memorial Sloan-Kettering Cancer Center, Manhattan, NY

GitHub Events

Total
  • Watch event: 1
Last Year
  • Watch event: 1

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 773
  • Total Committers: 7
  • Avg Commits per committer: 110.429
  • Development Distribution Score (DDS): 0.181
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Yuanqing Wang w****q@w****t 633
Josh Fass j****s@c****g 102
yuanqing-wang w****q@u****u 28
ChayaSt c****n@c****g 6
kunluo k****o@u****u 2
Flora Zhao z****g@F****l 1
Flora Zhao z****g@f****g 1
Committer Domains (Top 20 + Academic)
umich.edu: 2 choderalab.org: 2 floras-mbp-2.mskcc.org: 1 wangyq.net: 1

Dependencies

requirements.txt pypi
  • numpy *
  • pandas *
  • pytest *
  • rdkit *
  • sklearn *
  • tensorflow ==2.0.0