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W4_​Similarity_​search

W4_Similarity_search
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 measuressuch as the Tanimoto and Dice similarity. The following steps show how to use these encodings and comparisonmethods. 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://projects.volkamerlab.org/teachopencadd/talktorials/T004_compound_similarity.html DatasetDatasetTanimoto similarityDice similarityTanimoto similarityDice similarityTanimoto similarityDice similarityQueryQuerySimilarity to queryList of compoundsGefitinibNode 284Node 285Node 290 RDKit Fingerprint RDKit Fingerprint Similarity Search Similarity Search Similarity Search Similarity Search Column Rename Column Rename Column Rename Column Rename EnrichmentPlotter (local) EnrichmentPlotter (local) RDKit Fingerprint RDKit Fingerprint Molecule Type Cast RDKit From Molecule Scatter plot CSV Reader Query compound WorkflowService Input WorkflowService Input Joiner Joiner Joiner WorkflowService Output 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 measuressuch as the Tanimoto and Dice similarity. The following steps show how to use these encodings and comparisonmethods. 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://projects.volkamerlab.org/teachopencadd/talktorials/T004_compound_similarity.html DatasetDatasetTanimoto similarityDice similarityTanimoto similarityDice similarityTanimoto similarityDice similarityQueryQuerySimilarity to queryList of compoundsGefitinibNode 284Node 285Node 290 RDKit Fingerprint RDKit Fingerprint Similarity Search Similarity Search Similarity Search Similarity Search Column Rename Column Rename Column Rename Column Rename EnrichmentPlotter (local) EnrichmentPlotter (local) RDKit Fingerprint RDKit Fingerprint Molecule Type Cast RDKit From Molecule Scatter plot CSV Reader Query compound WorkflowService Input WorkflowService Input Joiner Joiner Joiner WorkflowService Output

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