Release 4.3

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

Release 4.2

New in REINVENT 4.2
For details see CHANGELOG.md.

Reworked TL code with added options and statistics
Standardization can be switched off in TL (useful in new prior creation)
Similarity calculation in TL made optional
Updated script for empty classical Reinvent model creation
Allow runs with only filter/penalty components
Stable sigmoid functions
Removed long chain check in SMILES processing
Unified transformer code
Filter apply to transformed scores
Better memory handling in inception
Better logging for Reinvent standardizer
Inception filters for tokens not compatible with the prior
Number of CPUs for TL (Mol2Mol pair generation) is 1 by default
Tensorboard histogram bug fixed again
Code improvements and fixes

Release 4.1

New in REINVENT 4.1

For details, see CHANGELOG.md.

• Scoring component MolVolume
• Scoring component for all 210 RDKit descriptors
• CSV and SMILES file reader for the scoring run mode
• Tobias Ploetz' (Merck) REINFORCE implementations of the DAP, MAULI and MASCOF RL reward strategies
• Number of CPUs can be specified for TL jobs: useful for Windows
• All prior models tagged with metadata and checked for integrity
• Code improvements and fixes

First Release 4.0

This is the first public release of the REINVENT4 series.