New in REINVENT 4.3
For details see CHANGELOG.md.
- Upgrade to PyTorch 2.2: rerun
pip install -r requirements-linux-64.lock - 2 new notebooks demoing Reinvent with reinforcement learning and also transfer learning, includes TensorBoard visualisation and basic analysis
- New Linkinvent model code based on unified transformer
- New PubChem Mol2Mol prior
- Unknown token support for PubChem based transformer models
- New "device" config parameter to allow for explicit device e.g. "cuda:0"
- Optional SMILES randomization in every TL epoch for Reinvent
- Dataclass parameter validation for most scoring components
- Invalid SMILES are now written to the reinforcement learning CSV
- Code improvements and fixes