MarkusFerdinandDablander/ECFP-Sort-and-Slice
Sort & Slice: A Simple and Superior Alternative to Hash-Based Folding for Extended-Connectivity Fingerprints (ECFPs)
When analyzing chemical compounds, this project helps you transform RDKit molecule objects into a numerical representation called Extended-Connectivity Fingerprints (ECFPs). It takes your RDKit molecule objects as input and produces a vector of numbers, making it easier to use these chemical structures in machine learning models for tasks like predicting molecular properties. Medicinal chemists, computational chemists, and cheminformaticians who work with molecular data for drug discovery or materials science would use this.
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Use this if you need to generate high-quality, vectorial ECFPs for molecular property prediction and other cheminformatics applications, aiming for better predictive performance than traditional methods.
Not ideal if you primarily work with other molecular representations or do not use RDKit for handling chemical structures.
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MIT
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Feb 13, 2025
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