Prepare release v.0.0.11

Changelog v0.0.11

Highlights

New Features & Enhancements

MolToGraph and GraphToMol

• Refactored source code for better modularity and maintainability.
• Introduced a lightweight version of MolToGraph and GraphToMol for efficiency.
• Added functionality to convert reaction SMILES into a graph representation.

Visualization

• Enabled 2D coordinate generation from RDKit molecules for improved user-friendly MolGraph and ITSGraph visualizations.
• Introduced a visualizer to process reaction SMILES with atom-atom mapping (AAM):
  • Outputs separate visualizations for reactants, ITS graphs, and products.
• Added the capability to save visualization outputs as PDFs for convenient sharing and documentation.

MØD Rule Application

• Introduced the Transformation subpackage to support MØD-based graph transformations.
• Enhanced compatibility with GitHub workflows by adding test cases for transformations.

PartialAAM Expansion (Preliminary Support)

• Added the SynAAM subpackage for expanding PartialAAM reaction SMILES (RSMI) into full AAM RSMI.
• Included a normalizer to reduce redundant hydrogen mappings and retain mappings only for hydrogens in the reaction center.
• Implemented ITS decomposer to split ITS graphs back into reactant and product graphs.

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Known Limitations

MØD and RDKit Compatibility Issues

• Problem: Conflicts between MØD (conda version) and RDKit lead to Boost.Python.ArgumentError when using RDKit.
• Workaround:
  • Add a workaround (using torch 2.0.1) before initializing MØD to mitigate compatibility issues.
  • For pytest, use a fixed test order to avoid conflicts during testing workflows.

v0.0.10: Prepare release v.0.0.10 (#11)

Changelog v0.0.10

New Release Highlights

Improvements

• Unified Graph Descriptors and Fingerprints
  Combined the functionalities of graph descriptors and graph fingerprints into a cohesive module, simplifying the codebase and enhancing efficiency in graph property calculations and structural comparisons.

• Parallel Processing for Descriptor Computation
  Introduced parallel processing using joblib to handle graph descriptor computation across multiple entries simultaneously. This significantly reduces runtime on multi-core systems, particularly beneficial for large datasets. Users can specify the number of parallel jobs (n_jobs) and verbosity levels for flexible performance tuning.

• MØD Rule Application (Preliminary)
  Added preliminary support for applying MØD (Modeling Organic Databases) rules within the graph transformation pipeline. While not fully tested due to dependency conflicts in conda, this feature provides a framework for chemical rule applications in future updates.

Bug Fixes

• Adapted MolToGraph for Unsanitized RDKit Molecules
  Updated MolToGraph to handle unsanitized RDKit Mol objects, allowing more flexibility with RDKit molecule inputs without requiring pre-sanitization. This fix addresses compatibility issues and expands support for RDKit functionalities within the graph processing pipeline.

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