dptech-corp/Uni-3DAR

Implementation of paper "Uni-3DAR: Unified 3D Generation and Understanding via Autoregression on Compressed Spatial Tokens"

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/ 100
Emerging

This project helps scientists and researchers in chemistry and materials science by generating and analyzing 3D molecular and crystal structures. It takes chemical formulas or properties as input and outputs detailed 3D atomic arrangements or predictions about their behavior. This is ideal for computational chemists, material scientists, and drug discovery researchers working with complex 3D structures.

145 stars. No commits in the last 6 months.

Use this if you need to generate novel 3D molecular or crystal structures, predict molecular properties, or understand protein-ligand interactions with high efficiency and accuracy.

Not ideal if you are working with non-atomic 3D data like architectural models or geological formations, or if you do not have access to GPU resources for training and inference.

molecular-design materials-discovery drug-discovery computational-chemistry crystal-structure-prediction
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 14 / 25

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Stars

145

Forks

16

Language

Python

License

MIT

Last pushed

Apr 21, 2025

Commits (30d)

0

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