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Machine Learning in Drug Discovery and Drug Design

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Academic year: 2021

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Figure 2.2: A typical workflow for constructing machine learning models based on raw data from drug discovery & design
Figure 3.1: Compounds used in modeling log D, projected onto the first two principle components obtained by linear PCA
Figure 3.2: Compounds used in modeling aqueous solubility, projected onto the first two principle components obtained by linear PCA
Figure 3.3: PCA visualizations of a dataset used to model the hERG channel blockade effect of molecules (represented by ChemaxonFP descriptors) show outliers with respect to the first two principle components (sparse cloud of points below the dense region)
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Learning Algorithms Evaluation Strategies Quantifying Domain of Applicability Presenting Results to Bench Chemists Examples & Discussion Related Work hERG Channel Blockade Effect Virtual Screening for PPAR-gamma Agonists How to Cite this Thesis

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