With the continuously increasing amount of available data, machine learning (ML) gained momentum in drug discovery and especially in ligand-based virtual screening (VS) to predict the activity of novel compounds against a target of interest. In this workflow, different ML models are trained on the filtered ChEMBL dataset to discriminate between active and inactive compounds with respect to a protein target.
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