概览介绍
Graph-neural-network tooling for chemistry and biology. Pin compatible DGL and framework versions; verify model generalization on the intended chemical space.
信息已比对权威文献或官方白皮书;非实验性跑分。 权威出处 · 最后核对: 16/09/2026, 17:33:40 UTC
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开发机构 AWS Labs / DGL-LifeSci contributors →
Graph-neural-network tooling for chemistry and biology. Pin compatible DGL and framework versions; verify model generalization on the intended chemical space.
Graph-neural-network tooling for chemistry and biology. Pin compatible DGL and framework versions; verify model generalization on the intended chemical space.
信息已比对权威文献或官方白皮书;非实验性跑分。 权威出处 · 最后核对: 16/09/2026, 17:33:40 UTC
Use this citation format when referencing DGL-LifeSci in scientific publications and benchmark papers.
@software{dgl_lifesci_2026,
title = {{DGL-LifeSci}},
author = {{AWS Labs / DGL-LifeSci contributors}},
year = {2026},
url = {https://github.com/awslabs/dgl-lifesci},
note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}Live scientific citations queried from Europe PMC and PubMed for DGL-LifeSci.
Missing values mean the catalog has no recorded information. They do not mean a feature is absent.
No software–hardware integration has been verified for this entry yet. Explore documented research workflows.

DGL-LifeSci Scientific Architecture · Credit: AWS Labs / DGL-LifeSci contributors · Open original ↗
Research and editorial use Source & attribution ↗ License ↗
同一科学领域的同类算法与工具。
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Rosetta macromolecular modeling suite — protein-structure-refinement / classical-protein-design
Research / method authors
暂无与此工具关联的已验证学术动态。
浏览全部生物AI动态 →See project access and maintenance status
费用根据官方最近一次公布的说明整理。请在官方网站确认最新报价与学术折扣。
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