
During November 2025, Sveccham developed and delivered the Cuik-molmaker molecular featurization recipe for the conda-forge/staged-recipes repository. This work enabled the conversion of chemical structures into graph representations suitable for deep learning models, with a focus on robust packaging and cross-platform compatibility. Sveccham integrated C++ and Python components, incorporated librdkit as a runtime dependency, and enhanced build configurations using YAML and Conda. The project included expanded test coverage for feature extraction workflows and ensured reliable operation of console scripts on both Windows and non-Windows systems, resulting in improved reproducibility and readiness for downstream molecular modeling and machine learning applications.
November 2025: Delivered the Cuik-molmaker molecular featurization recipe with packaging and testing enhancements for conda-forge/staged-recipes. Implemented cross-platform packaging (Windows compatibility), integrated librdkit into the run-time environment, updated checksums, and validated console scripts across Windows and non-Windows systems. Expanded test coverage with new cuik-molmaker tests (fit distribution and mol-features) and updated build configurations (stdlib in build, added pybind11-abi). Adjusted repository location and branch references to support stabilized CI across platforms.
November 2025: Delivered the Cuik-molmaker molecular featurization recipe with packaging and testing enhancements for conda-forge/staged-recipes. Implemented cross-platform packaging (Windows compatibility), integrated librdkit into the run-time environment, updated checksums, and validated console scripts across Windows and non-Windows systems. Expanded test coverage with new cuik-molmaker tests (fit distribution and mol-features) and updated build configurations (stdlib in build, added pybind11-abi). Adjusted repository location and branch references to support stabilized CI across platforms.

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