gnina
importedtherapeutics/gnina
A deep learning framework for molecular docking
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Therapeutics
- Subcategory
- unknown
- License
- Apache-2.0(osi)
- Status
- active
- Maturity
- deployed
- Organization
- gnina
- Country
- unknown
- Homepage
- unknown
- Repository
- github.com/gnina/gnina
- Documentation
- unknown
- Tags
- cheminformatics · computational-chemistry · convolutional-neural-networks · drug-discovery · molecular-modeling
- Regulatory
- unknown
Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- sensaascheminformatics · computational-chemistry · molecular-modeling
Shape-based alignment of molecules using 3D point-based representation
- stkocheminformatics · computational-chemistry · molecular-modeling
A collection of molecular optimisers and property calculators for use with stk.
- ReLeaSEcheminformatics · drug-discovery · molecular-modeling
Deep Reinforcement Learning for de-novo Drug Design
- Bentocheminformatics · computational-chemistry · drug-discovery
UV-first benchmark for protein-ligand docking with reproducible annotation, pocket similarity, and HPC workflows.
- DockM8cheminformatics · computational-chemistry · drug-discovery
All in one Structure-Based Virtual Screening workflow based on the concept of consensus docking.
- Meekodrug-discovery · molecular-modeling
Interface for AutoDock, molecule parameterization
- api.github.com/repos/gnina/gninaretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2026-06-29, 959 stars, license reported as Apache-2.0. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/2.json→ .entries["gnina"]
Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.