Observed since 20 Oct, 18:07 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
A playground for applying graph convolutional networks to molecules.
A playground for applying graph convolutional networks to molecules, with a focus on learning continuous "atom-type" embeddings and from these classical molecule force field parameters.
Note: This project is still in development and liable to substantial API and other changes.
This framework is mostly based upon the End-to-End Differentiable Molecular Mechanics Force Field Construction preprint by Wang, Fass and Chodera.
NAGL is bound by a Code of Conduct.
See our documentation for notes on installation, basic usage, theory, and examples!
The NAGL source code is hosted at https://github.com/openforcefield/openff-nagl and is available under the MIT license (see the file LICENSE). Some parts inherit from code distributed under other licenses, as detailed in LICENSE-3RD-PARTY).
NAGL inherits from Simon Boothroyd's NAGL library at https://github.com/SimonBoothroyd/nagl.
Download counts and popularity will appear here as the collector receives them.
| Version | Published | Direct dependencies | Open version |
|---|---|---|---|
| 0.5.5 | 18 May 2026 | 4 |
This describes our collected data; it is not a security assessment.
Shared repository data · GitHub
Public sponsor counts have not been collected. These signals belong to the repository owner.
github · 1 observations shown. Missing days are unknown.
| Date / source period | Value | Observed at |
|---|---|---|
| 2026-10-21 | 9 | 2026-10-21T17:13:40.8275672Z |
Observed since 20 Oct, 18:07 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
package:18b420f7e3562129dca4b2e2a35377ed6fb0092475bc71ec9e9e5fa4f9a1f963