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⚡ What’s New in Scikit-Mol 0.4.6?
1️⃣ Faster Parallel Processing:
Migrated from multiprocessing to joblib, fixing performance issues on Windows & Mac.
✅ Test times down from ~7 mins to ~1.5 mins on Windows!
✅ More robust, easier to maintain code.
⚡ What’s New in Scikit-Mol 0.4.6?
1️⃣ Faster Parallel Processing:
Migrated from multiprocessing to joblib, fixing performance issues on Windows & Mac.
✅ Test times down from ~7 mins to ~1.5 mins on Windows!
✅ More robust, easier to maintain code.
Comments
2️⃣ API Update – Consistency with scikit-learn:
• parallel → n_jobs (matches scikit-learn API)
• If you used parallel=True, switch to n_jobs=-1 for full CPU usage.
3️⃣ Bug Fix:
Fixed a potential issue with safe_inference in parallel runs.
#ScikitLearn #RDKit
4️⃣ Deprecations & Build System Updates:
• Python 3.8 is now deprecated.
• Switched to uv & hatchling for build/dependency management (see https://CONTRIBUTING.md).
• Added https://plotting.py for benchmarks + updated Notebook 7 with new benchmarks.
💡 We’d Love Your Feedback!
Are you using Scikit-Mol in your projects? Got ideas, feature requests, or want to contribute?
Let’s make Scikit-Mol even better—together. 🙌
#Cheminformatics #MachineLearning #OpenSource #Python #RDKit #ScikitLearn #ScikitMol