generative-design

Generate and optimise novel small molecules with REINVENT 4 — de novo sampling from a chemical language model, scaffold decoration with LibInvent, fragment linking with LinkInvent, and similarity-constrained analogue generation with Mol2Mol. Use this skill to set up reinforcement-learning or curriculum runs, compose a multi-parameter scoring function from docking scores, predictive models, and physicochemical desirability, and read the resulting sampled sets. Also trigger on REINVENT, LibInvent, LinkInvent, Mol2Mol, scaffold hopping, R-group replacement, linker design, chemical language model, or reinforcement-learning molecule optimisation.

Version

1.0

License

MIT

Compatibility

Requires Python 3.10+. The bundled scripts build REINVENT TOML configuration and parse sampled CSV output using only the standard library. Running a generation job needs REINVENT 4 installed from github.com/MolecularAI/REINVENT4 (not on PyPI, Apache-2.0) with its model priors, and an NVIDIA GPU for practical reinforcement-learning throughput.