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self learning and reference material on QSAR moleculear modelling

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Notes and reference material on molecular modelling and QSAR

Oscar Charles 2023

This is a set of material that introduces QSAR molecular modelling concepts.

Notebooks:

  • 1 - Conceptual and light introduction to QSAR & modelling.
  • 2 - Discussion and comparison of types of molecular fingerprints and code to compare metrics in a LogP and HIV1 protase bioactivity datasets.
  • 3 - Comparison of fingerprints and ML algorithms, plus ensembles.
  • 4 -

There will be a consistent toolset used in these notebooks: pandas numpy xgboost sklearn matplotlib rdkit deepchem mordred tensorflow scipy seaborn opencv-python umap-learn

The example data will come from CHEMBL: logp data source CHEMBL635482

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