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TeachOpenCADD_​reconfigured_​with_​KNIME_​4.5

TeachOpenCADD_reconfigured_with_KNIME_4.5
TeachOpenCADD: A teaching platform for computer-aided drug design using KNIME 4.5 W1 W4 W2 W5 W6 W7 W3 W8 Dominique Sydow, Michele Wichmann, Jaime Rodríguez-Guerra, Daria Goldmann, Gregory Landrum, andAndrea Volkamer 1. Data acquisition from ChEMBL 2. Molecular filtering: ADME and lead-likeness criteria 3. Molecular filtering: unwantedsubstructures 4. Ligand-based screening:compound similarity 5. Compound clustering 6. Maximum common substructures 7. Ligand-based screening: machinelearning Dataset filtered by X-ray,resolution & presence of ligandNote: Target ChEMBL ID can only be changed inside the metanodeTanimoto similarityHighlighted MCSFiltered compound setScore viewGefitinibDiverse subset from clustersROC curveSimilarity to query Compound setW1Dataset filtered & formatted bybioactivity and SMILESNote: Database query can be slow.W2Dataset filtered by Lipinski's rule of fivePhys. chem. propertiesW3Dataset filtered byunwanted substructuresCompounds with PAINS/BrenkCompounds without PAINS/BrenkW4Dataset ranked bysimilarity to query compoundW5Dataset clustered byFingerprint similarityNote: Clustering takes time!W6Get MCS for largest cluster in datasetW7ML classifier(applicable to new compounds) 8. Protein data acquisition:Protein Data Bank (PDB) EnrichmentPlotter (local) Input targetChEMBL ID Table View Table View Evaluate model Query compound Table View Line Plot Evaluate model Scatter plot Table View Workflow Reader Workflow Executor Workflow Reader Workflow Executor Box Plot Workflow Reader Workflow Executor Table View Table View Workflow Reader Workflow Executor Workflow Reader Workflow Executor Workflow Reader Workflow Executor Workflow Reader Workflow Executor TeachOpenCADD: A teaching platform for computer-aided drug design using KNIME 4.5 W1 W4 W2 W5 W6 W7 W3 W8 Dominique Sydow, Michele Wichmann, Jaime Rodríguez-Guerra, Daria Goldmann, Gregory Landrum, andAndrea Volkamer 1. Data acquisition from ChEMBL 2. Molecular filtering: ADME and lead-likeness criteria 3. Molecular filtering: unwantedsubstructures 4. Ligand-based screening:compound similarity 5. Compound clustering 6. Maximum common substructures 7. Ligand-based screening: machinelearning Dataset filtered by X-ray,resolution & presence of ligandNote: Target ChEMBL ID can only be changed inside the metanodeTanimoto similarityHighlighted MCSFiltered compound setScore viewGefitinibDiverse subset from clustersROC curveSimilarity to query Compound setW1Dataset filtered & formatted bybioactivity and SMILESNote: Database query can be slow.W2Dataset filtered by Lipinski's rule of fivePhys. chem. propertiesW3Dataset filtered byunwanted substructuresCompounds with PAINS/BrenkCompounds without PAINS/BrenkW4Dataset ranked bysimilarity to query compoundW5Dataset clustered byFingerprint similarityNote: Clustering takes time!W6Get MCS for largest cluster in datasetW7ML classifier(applicable to new compounds) 8. Protein data acquisition:Protein Data Bank (PDB) EnrichmentPlotter (local) Input targetChEMBL ID Table View Table View Evaluate model Query compound Table View Line Plot Evaluate model Scatter plot Table View Workflow Reader Workflow Executor Workflow Reader Workflow Executor Box Plot Workflow Reader Workflow Executor Table View Table View Workflow Reader Workflow Executor Workflow Reader Workflow Executor Workflow Reader Workflow Executor Workflow Reader Workflow Executor

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Created by: michele
Created at: 2019-01-10
On NodePit since: 2022-01-28
Last update: 2024-04-16
Created with KNIME version: v4.5.1
Tags: ChEMBL queryPDB queryMolecular filteringUnwanted substructuresLipinski's rule of fiveSimilarity searchCompound clusteringMaximum common substructureMachine learningTeachOpenCADD

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