https://github.com/awslabs/dgl-lifesci

Python package for graph neural networks in chemistry and biology

https://github.com/awslabs/dgl-lifesci

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
    3 of 22 committers (13.6%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (15.2%) to scientific vocabulary

Keywords

bioinformatics cheminformatics deep-learning dgl drug-discovery geometric-deep-learning graph-neural-networks molecule

Keywords from Contributors

transformers biology materials-science quantum-chemistry
Last synced: 6 months ago · JSON representation

Repository

Python package for graph neural networks in chemistry and biology

Basic Info
  • Host: GitHub
  • Owner: awslabs
  • License: apache-2.0
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 963 KB
Statistics
  • Stars: 770
  • Watchers: 15
  • Forks: 160
  • Open Issues: 32
  • Releases: 8
Topics
bioinformatics cheminformatics deep-learning dgl drug-discovery geometric-deep-learning graph-neural-networks molecule
Created almost 6 years ago · Last pushed over 2 years ago
Metadata Files
Readme Contributing License Code of conduct

README.md

DGL-LifeSci

Documentation | Discussion Forum

We also have a slack channel for real-time discussion. If you want to join the channel, contact mufeili1996@gmail.com.

Table of Contents

Introduction

Deep learning on graphs has been an arising trend in the past few years. There are a lot of graphs in life science such as molecular graphs and biological networks, making it an import area for applying deep learning on graphs. DGL-LifeSci is a DGL-based package for various applications in life science with graph neural networks.

We provide various functionalities, including but not limited to methods for graph construction, featurization, and evaluation, model architectures, training scripts and pre-trained models.

For a list of community contributors, see here.

Installation

Requirements

DGL-LifeSci should work on

  • all Linux distributions no earlier than Ubuntu 16.04
  • macOS X
  • Windows 10

It is recommended to create a conda environment for DGL-LifeSci with for example

conda create -n dgllife python=3.6

DGL-LifeSci requires python 3.6+, DGL 0.7.0+ and PyTorch 1.5.0+.

Install pytorch

Install dgl

Additionally, we require RDKit. The easiest way to install RDKit is

pip install rdkit

If you need to work on the example of JTVAE, then you need RDKit 2018.09.3. We recommend installing it with

conda install -c rdkit rdkit==2018.09.3

For other installation recipes for RDKit, see the official documentation.

Pip installation for DGL-LifeSci

pip install dgllife

Installation from source

If you want to try experimental features, you can install from source as follows:

git clone https://github.com/awslabs/dgl-lifesci.git cd dgl-lifesci/python python setup.py install

Verifying successful installation

Once you have installed the package, you can verify the success of installation with

```python import dgllife

print(dgllife.version)

0.3.2

```

Command Line Interface

DGL-LifeSci provides command line interfaces that allow users to perform modeling without any background in programming and deep learning. You will need to first clone the github repo.

Examples

For a full list of work implemented in DGL-LifeSci, see here.

Cite

If you use DGL-LifeSci in a scientific publication, we would appreciate citations to the following paper:

@article{dgllife, title={DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science}, author={Mufei Li and Jinjing Zhou and Jiajing Hu and Wenxuan Fan and Yangkang Zhang and Yaxin Gu and George Karypis}, year={2021}, journal = {ACS Omega} }

Owner

  • Name: Amazon Web Services - Labs
  • Login: awslabs
  • Kind: organization
  • Location: Seattle, WA

AWS Labs

GitHub Events

Total
  • Issues event: 1
  • Watch event: 45
  • Fork event: 13
Last Year
  • Issues event: 1
  • Watch event: 45
  • Fork event: 13

Committers

Last synced: 12 months ago

All Time
  • Total Commits: 229
  • Total Committers: 22
  • Avg Commits per committer: 10.409
  • Development Distribution Score (DDS): 0.131
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Mufei Li m****6@g****m 199
Yangkang Zhang 4****8 4
autodataming 1****4@q****m 3
Martin Volk m****k@g****t 3
Krishna Sirumalla s****s@h****u 2
Vignesh Venkataraman m****n@p****n 2
Andrew Stolman a****n@k****m 1
Amazon GitHub Automation 5****o 1
Eric O. Korman e****n@g****m 1
In-Ho Yi c****h@g****m 1
Jinjing Zhou V****n 1
Joe j****e@p****p 1
JoshuaMeyers j****s@r****m 1
Marcos Leal m****4@y****r 1
Pavol Drotar p****1@c****k 1
Philipp A f****p@w****e 1
Raymond Gasper 1****r 1
Sooheon Kim s****n@f****i 1
Wenxuan Fan 4****0 1
YueZhong 3****o 1
cyhFlight 3****t 1
xnouhz x****z@1****m 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 62
  • Total pull requests: 46
  • Average time to close issues: about 1 month
  • Average time to close pull requests: about 1 month
  • Total issue authors: 50
  • Total pull request authors: 15
  • Average comments per issue: 3.19
  • Average comments per pull request: 1.0
  • Merged pull requests: 40
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 2
  • Pull request authors: 0
  • Average comments per issue: 1.5
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • andt88 (3)
  • ph-mehdi (3)
  • mar-volk (3)
  • nbrosse (3)
  • kangqiyue (2)
  • VIGNESHinZONE (2)
  • dywlkji (2)
  • maryamag85 (2)
  • lvkd84 (1)
  • rajarshiche (1)
  • ndickson-nvidia (1)
  • amblee0306 (1)
  • GattiMh (1)
  • kexinhuang12345 (1)
  • jadolfbr (1)
Pull Request Authors
  • mufeili (26)
  • mar-volk (3)
  • astolman (2)
  • chajath (2)
  • marcossilva (2)
  • VIGNESHinZONE (2)
  • xnuohz (1)
  • ekorman (1)
  • flying-sheep (1)
  • wenx00 (1)
  • yxgu2353 (1)
  • rgasper (1)
  • jacobumland (1)
  • cyhFlight (1)
  • xuzijian629 (1)
Top Labels
Issue Labels
bug (2) enhancement (1)
Pull Request Labels

Packages

  • Total packages: 3
  • Total downloads:
    • pypi 13,460 last-month
  • Total docker downloads: 74
  • Total dependent packages: 8
    (may contain duplicates)
  • Total dependent repositories: 36
    (may contain duplicates)
  • Total versions: 26
  • Total maintainers: 1
pypi.org: dgllife

DGL-based package for Life Science

  • Versions: 17
  • Dependent Packages: 8
  • Dependent Repositories: 29
  • Downloads: 13,460 Last month
  • Docker Downloads: 74
Rankings
Dependent packages count: 2.1%
Stargazers count: 2.5%
Dependent repos count: 2.7%
Average: 2.9%
Downloads: 3.1%
Forks count: 4.1%
Maintainers (1)
Last synced: 6 months ago
proxy.golang.org: github.com/awslabs/dgl-lifesci
  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 9.0%
Average: 9.6%
Dependent repos count: 10.2%
Last synced: 6 months ago
conda-forge.org: dgllife
  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 7
Rankings
Dependent repos count: 12.8%
Forks count: 15.9%
Stargazers count: 16.7%
Average: 24.2%
Dependent packages count: 51.5%
Last synced: 6 months ago

Dependencies

python/setup.py pypi
  • hyperopt *
  • joblib *
  • networkx >=2.1
  • numpy >=1.14.0
  • pandas *
  • requests >=2.22.0
  • scikit-learn >=0.22.2,
  • scipy >=1.1.0
  • tqdm *