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TeachOpenCADD_​Workflow2_​ADME_​filter

TeachOpenCADD Workflow 2: Molecular filtering: ADME and lead-likeness criteria

Not all compounds are suitable starting points for drug development due to undesirable pharmacokinetic properties, which for instance negatively affect a drug's absorption, distribution, metabolism, and excretion (ADME). Therefore, such compounds are usually not included in data sets for virtual screening.
This workflow shows how to remove less drug-like molecules from a data set using Lipinski's rule of five.

2. Molecular filtering: ADME and lead-likeness criteriaNot all compounds are suitable starting points for drug developmentdue to undesirable pharmacokinetic properties, which for instancenegatively affect a drug's absorption, distribution, metabolism, andexcretion (ADME). Therefore, such compounds are usually not includedin data sets for virtual screening. The following steps show how toremove less drug-like molecules from the data set. Step 3Visualize dataset properties with box plot Step 2Filter dataset by Lipinski's rule of five Step 1Calculate MW, HBD, HBA, and LogP This workflow is part of the TeachOpenCADD pipeline: https://hub.knime.com/volkamerlab/space/TeachOpenCADDRead more on the theoretical background of this workflow on our TeachOpenCADD platform: https://projects.volkamerlab.org/teachopencadd/talktorials/T002_compound_adme.html Calculate propertiesGet compounds with >= 3 fulfilled rulesHBD <= 5MW <= 500logP <=5HBA <=10HBA/2MW/100Calculate mean andstandard deviation Filtered compound setFiltered compound setAdd all compounds to one groupList of compoundsColumn Filter RDKit From Molecule Molecule Type Cast RDKit DescriptorCalculation Box Plot Column Aggregator Numeric RowSplitter Math Formula Math Formula Math Formula Math Formula Math Formula Math Formula GroupBy Table View CSV Writer ConstantValue Column CSV Reader 2. Molecular filtering: ADME and lead-likeness criteriaNot all compounds are suitable starting points for drug developmentdue to undesirable pharmacokinetic properties, which for instancenegatively affect a drug's absorption, distribution, metabolism, andexcretion (ADME). Therefore, such compounds are usually not includedin data sets for virtual screening. The following steps show how toremove less drug-like molecules from the data set. Step 3Visualize dataset properties with box plot Step 2Filter dataset by Lipinski's rule of five Step 1Calculate MW, HBD, HBA, and LogP This workflow is part of the TeachOpenCADD pipeline: https://hub.knime.com/volkamerlab/space/TeachOpenCADDRead more on the theoretical background of this workflow on our TeachOpenCADD platform: https://projects.volkamerlab.org/teachopencadd/talktorials/T002_compound_adme.html Calculate propertiesGet compounds with >= 3 fulfilled rulesHBD <= 5MW <= 500logP <=5HBA <=10HBA/2MW/100Calculate mean andstandard deviation Filtered compound setFiltered compound setAdd all compounds to one groupList of compoundsColumn Filter RDKit From Molecule Molecule Type Cast RDKit DescriptorCalculation Box Plot Column Aggregator Numeric RowSplitter Math Formula Math Formula Math Formula Math Formula Math Formula Math Formula GroupBy Table View CSV Writer ConstantValue Column CSV Reader

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: dominique
Created at: 2019-07-15
On NodePit since: 2019-10-03
Last update: 2024-04-23
Created with KNIME version: v4.5.0
Tags: Lipinski's rule of fiveADMEDrug lead-likenessMolecular filtering

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