tufts-ml/G2PT

Graph generative pre-trained transformer

32
/ 100
Emerging

This framework helps chemists and materials scientists design novel molecules by generating new molecular structures from scratch. You provide a starting point, and it outputs a diverse set of valid molecular graphs. It's ideal for researchers in drug discovery or materials science seeking to explore chemical space.

No commits in the last 6 months.

Use this if you need to generate new molecular structures for drug discovery or materials science applications.

Not ideal if you need to analyze or predict properties of existing molecules, rather than generate new ones.

drug-discovery materials-science molecular-design cheminformatics chemical-synthesis
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 8 / 25

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Stars

19

Forks

2

Language

Python

License

MIT

Last pushed

Jun 03, 2025

Commits (30d)

0

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