jRicciL/ML-ensemble-docking
Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning
This project helps medicinal chemists and computational biologists improve drug discovery by more accurately identifying potential drug candidates. It takes in protein structural data (ensembles) and small molecule libraries, then applies machine learning to refine the ranking of which molecules are most likely to bind effectively. This is for researchers performing virtual screening to prioritize compounds for experimental testing.
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Use this if you are performing structure-based virtual screening with ensemble docking and need a more reliable way to rank potential drug ligands.
Not ideal if you are looking for a general-purpose molecular docking tool or do not have access to protein conformational ensembles.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Apr 06, 2022
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