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NEWS.md

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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

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

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

New in REINVENT 4.0

  • Combined RL/CL (staged learning)
  • New transformer model for molecule optimization
  • Full integration of all generators with all algorithmic frameworks (RL, TL)
  • Reworked scoring component utilizing a plugin mechanism for easy extension
  • TOML configuration file format in addition to JSON: note that format is incompatible with release 3.x
  • Major code rewrite
  • Single repository for all code