{"id":13689221,"url":"https://github.com/awslabs/dgl-lifesci","last_synced_at":"2025-05-15T09:08:52.155Z","repository":{"id":37826112,"uuid":"258124207","full_name":"awslabs/dgl-lifesci","owner":"awslabs","description":"Python package for graph neural networks in chemistry and biology","archived":false,"fork":false,"pushed_at":"2023-11-01T19:32:07.000Z","size":986,"stargazers_count":751,"open_issues_count":31,"forks_count":160,"subscribers_count":15,"default_branch":"master","last_synced_at":"2025-05-09T23:40:06.252Z","etag":null,"topics":["bioinformatics","cheminformatics","deep-learning","dgl","drug-discovery","geometric-deep-learning","graph-neural-networks","molecule"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/awslabs.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2020-04-23T07:14:21.000Z","updated_at":"2025-04-22T23:04:29.000Z","dependencies_parsed_at":"2023-12-26T08:45:52.752Z","dependency_job_id":"a6f19f79-7e6c-456b-9825-c6787d2edeb5","html_url":"https://github.com/awslabs/dgl-lifesci","commit_stats":{"total_commits":229,"total_committers":23,"mean_commits":9.956521739130435,"dds":"0.13100436681222705","last_synced_commit":"be8bc71d29ecf34a9dab7c7bd47c08f3383d9be0"},"previous_names":[],"tags_count":8,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/awslabs%2Fdgl-lifesci","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/awslabs%2Fdgl-lifesci/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/awslabs%2Fdgl-lifesci/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/awslabs%2Fdgl-lifesci/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/awslabs","download_url":"https://codeload.github.com/awslabs/dgl-lifesci/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254310520,"owners_count":22049470,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bioinformatics","cheminformatics","deep-learning","dgl","drug-discovery","geometric-deep-learning","graph-neural-networks","molecule"],"created_at":"2024-08-02T15:01:38.678Z","updated_at":"2025-05-15T09:08:47.146Z","avatar_url":"https://github.com/awslabs.png","language":"Python","funding_links":[],"categories":["Python","Libraries"],"sub_categories":["Machine Learning"],"readme":"# DGL-LifeSci\n\n[Documentation](https://lifesci.dgl.ai/index.html) | [Discussion Forum](https://discuss.dgl.ai)\n\nWe also have a **slack channel** for real-time discussion. If you want to join the channel, contact mufeili1996@gmail.com.\n\n## Table of Contents\n\n- [Introduction](#introduction)\n- [Installation](#installation)\n  * [Requirements](#requirements)\n  * [Pip installation for DGL-LifeSci](#pip-installation-for-dgl-lifesci)\n  * [Installation from source](#installation-from-source)\n  * [Verifying successful installation](#verifying-successful-installation)\n- [Command Line Interface](#command-line-interface)\n- [Examples](#examples)\n- [Cite](#cite)\n\n## Introduction\n\nDeep learning on graphs has been an arising trend in the past few years. There are a lot of graphs in\nlife science such as molecular graphs and biological networks, making it an import area for applying\ndeep learning on graphs. DGL-LifeSci is a DGL-based package for various applications in life science\nwith graph neural networks.\n\nWe provide various functionalities, including but not limited to methods for graph construction,\nfeaturization, and evaluation, model architectures, training scripts and pre-trained models.\n\nFor a list of community contributors, see [here](CONTRIBUTORS.md).\n\n## Installation\n\n### Requirements\n\nDGL-LifeSci should work on\n\n* all Linux distributions no earlier than Ubuntu 16.04\n* macOS X\n* Windows 10\n\nIt is recommended to create a conda environment for DGL-LifeSci with for example\n\n```\nconda create -n dgllife python=3.6\n```\n\nDGL-LifeSci requires python 3.6+, DGL 0.7.0+ and PyTorch 1.5.0+.\n\n[Install pytorch](https://pytorch.org/get-started/locally/)\n\n[Install dgl](https://www.dgl.ai/pages/start.html)\n\n\nAdditionally, we require `RDKit`. The easiest way to install RDKit is\n\n```\npip install rdkit\n```\n\nIf you need to work on the example of JTVAE, then you need `RDKit 2018.09.3`. We recommend installing it with\n\n```\nconda install -c rdkit rdkit==2018.09.3\n```\n\nFor other installation recipes for RDKit, see the [official documentation](https://www.rdkit.org/docs/Install.html).\n\n### Pip installation for DGL-LifeSci\n\n```\npip install dgllife\n```\n\n### Installation from source\n\nIf you want to try experimental features, you can install from source as follows:\n\n```\ngit clone https://github.com/awslabs/dgl-lifesci.git\ncd dgl-lifesci/python\npython setup.py install\n```\n\n### Verifying successful installation\n\nOnce you have installed the package, you can verify the success of installation with\n\n```python\nimport dgllife\n\nprint(dgllife.__version__)\n# 0.3.2\n```\n\n## Command Line Interface\n\nDGL-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.\n\n- [Molecular Property Prediction](examples/property_prediction/csv_data_configuration/)\n- [Reaction Prediction](examples/reaction_prediction/rexgen_direct/)\n\n## Examples\n\nFor a full list of work implemented in DGL-LifeSci, see [here](examples/README.md).\n\n## Cite\n\nIf you use DGL-LifeSci in a scientific publication, we would appreciate citations to the following paper:\n\n```\n@article{dgllife,\n    title={DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science},\n    author={Mufei Li and Jinjing Zhou and Jiajing Hu and Wenxuan Fan and Yangkang Zhang and Yaxin Gu and George Karypis},\n    year={2021},\n    journal = {ACS Omega}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fawslabs%2Fdgl-lifesci","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fawslabs%2Fdgl-lifesci","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fawslabs%2Fdgl-lifesci/lists"}