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TeachOpenCADD_​Workflow4_​Similarity_​search

Workflow

TeachOpenCADD Workflow 4: Ligand-based screening: Compound similarity

In virtual screening (VS), compounds similar to known ligands of a target under investigation often build the starting point for drug development. This approach follows the similar property principle stating that structurally similar compounds are more likely to exhibit similar biological activities (exceptions are so-called activity cliffs). For computational representation and processing, compound properties can be encoded in form of bit arrays, so-called molecular fingerprints, e.g. MACCS and Morgan fingerprints. Compound similarity can be assessed by measures such as the Tanimoto and Dice similarity. <br>This workflow shows how to use these encodings and comparison methods. VS is here conducted based on a similarity search.

Similarity searchTanimoto similarityDice similarityMorgan fingerprintMACCS fingerprintLigand-based screening
4. Ligand-based screening: compound similarityIn virtual screening (VS), compounds similar to known ligands of a target under investigation often build the startingpoint for drug development. This approach follows the similar property principle stating that structurally similarcompounds are more likely to exhibit similar biological activities (exceptions are so-called activity cliffs). Forcomputational representation and processing, compound properties can be encoded in form of bit arrays, so-calledmolecular fingerprints, e.g. MACCS and Morgan fingerprints. Compound similarity can be assessed by measures suchas the Tanimoto and Dice similarity. The following steps show how to use these encodings and comparison methods.VS is here conducted based on a similarity search. Step 2Similarity search of query compound (example: gefitinib) against full dataset using Tanimoto/Dice similarity Step 1Calculate fingerprints for datasetand query compound Step 3Evaluate performance with enrichmentplots (split dataset into active andinactive compounds at pIC50 = 6.3) MACC fingerprints Morgan fingerprints This workflow is part of theTeachOpenCADD pipeline: https://hub.knime.com/volkamerlab/space/TeachOpenCADDRead more on the theoreticalbackground of this workflow on ourTeachOpenCADD platform: https://github.com/volkamerlab/TeachOpenCADD/blob/master/talktorials/4_mol_similarity/T4_mol_similarity.ipynb DatasetDatasetTanimoto similarityDice similarityTanimoto similarityDice similarityTanimoto similarityDice similarityQueryQueryList of compoundsGefitinibSimilarity to query RDKit Fingerprint RDKit Fingerprint Similarity Search Similarity Search Similarity Search Similarity Search Column Rename Column Rename Column Rename Column Rename Joiner Joiner Joiner EnrichmentPlotter (local) EnrichmentPlotter (local) RDKit Fingerprint RDKit Fingerprint CSV Reader Molecule Type Cast RDKit From Molecule Query compound Scatter plot 4. Ligand-based screening: compound similarityIn virtual screening (VS), compounds similar to known ligands of a target under investigation often build the startingpoint for drug development. This approach follows the similar property principle stating that structurally similarcompounds are more likely to exhibit similar biological activities (exceptions are so-called activity cliffs). Forcomputational representation and processing, compound properties can be encoded in form of bit arrays, so-calledmolecular fingerprints, e.g. MACCS and Morgan fingerprints. Compound similarity can be assessed by measures suchas the Tanimoto and Dice similarity. The following steps show how to use these encodings and comparison methods.VS is here conducted based on a similarity search. Step 2Similarity search of query compound (example: gefitinib) against full dataset using Tanimoto/Dice similarity Step 1Calculate fingerprints for datasetand query compound Step 3Evaluate performance with enrichmentplots (split dataset into active andinactive compounds at pIC50 = 6.3) MACC fingerprints Morgan fingerprints This workflow is part of theTeachOpenCADD pipeline: https://hub.knime.com/volkamerlab/space/TeachOpenCADDRead more on the theoreticalbackground of this workflow on ourTeachOpenCADD platform: https://github.com/volkamerlab/TeachOpenCADD/blob/master/talktorials/4_mol_similarity/T4_mol_similarity.ipynb DatasetDatasetTanimoto similarityDice similarityTanimoto similarityDice similarityTanimoto similarityDice similarityQueryQueryList of compoundsGefitinibSimilarity to query RDKit Fingerprint RDKit Fingerprint Similarity Search Similarity Search Similarity Search Similarity Search Column Rename Column Rename Column Rename Column Rename Joiner Joiner Joiner EnrichmentPlotter (local) EnrichmentPlotter (local) RDKit Fingerprint RDKit Fingerprint CSV Reader Molecule Type Cast RDKit From Molecule Query compound Scatter plot

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Nodes

TeachOpenCADD_​Workflow4_​Similarity_​search consists of the following 27 nodes(s):

Plugins

TeachOpenCADD_​Workflow4_​Similarity_​search contains nodes provided by the following 5 plugin(s):