Icon

DrugRepurposingPipeline_​OpenTargets

1. Specification of the associated drugtargets 2. Retrieval of protein-ligandstructural data from theProtein Data Bank 4. Substructure searches to identifypotentially interesting compounds fordrug repurposing 3. Fetching ligand bioactivity data fromopen bioactivity data sources viaprogrammatic data access Biomedical information mining is increasingly recognized as a promising technique to accelerate drug discovery and development. Especially, integrative approaches which minedata from several (open) data sources have become more attractive with the increasing possibilities to programmatically access data through Application Programming Interfaces(APIs). The use of open data in conjunction with free, platform-independent analytic tools provides the additional advantage of flexibility, re-usability, and transparency. Here, wepresent a strategy for performing ligand-based in silico drug repurposing with the analytics platform KNIME. We demonstrate the usefulness of the developed workflow on the basisof two different use cases: a rare disease (here: Glucose Transporter Type 1 (GLUT-1) deficiency), and a new disease (here: COVID 19). The workflow includes a targeteddownload of data through web services, data curation, detection of enriched structural patterns, as well as substructure searches in DrugBank and a recently deposited data set ofantiviral drugs provided by Chemical Abstracts Service. Developed workflows, tutorials with detailed step-by-step instructions, and the information gained by the analysis of data forGLUT-1 deficiency syndrome and COVID-19 are made freely available to the scientific community. The provided framework can be reused by researchers for other in silico drugrepurposing projects, and it should serve as a valuable teaching resource for conveying integrative data mining strategies.Find more about the workflow in our paper:Tuerkova, A., Zdrazil, B. A ligand-based computational drug repurposing pipeline using KNIME and Programmatic Data Access: case studies for rare diseases and COVID-19. JCheminform 12, 71 (2020). https://doi.org/10.1186/s13321-020-00474-z Integrative Data Mining fromChEMBL, PubChem, and IUPHAR Concatenate Column Rename Open Target IDs toUniProt IDs Mapping PDB data retrieval Substructure searches inDrugBank and CAS dataset 1. Specification of the associated drugtargets 2. Retrieval of protein-ligandstructural data from theProtein Data Bank 4. Substructure searches to identifypotentially interesting compounds fordrug repurposing 3. Fetching ligand bioactivity data fromopen bioactivity data sources viaprogrammatic data access Biomedical information mining is increasingly recognized as a promising technique to accelerate drug discovery and development. Especially, integrative approaches which minedata from several (open) data sources have become more attractive with the increasing possibilities to programmatically access data through Application Programming Interfaces(APIs). The use of open data in conjunction with free, platform-independent analytic tools provides the additional advantage of flexibility, re-usability, and transparency. Here, wepresent a strategy for performing ligand-based in silico drug repurposing with the analytics platform KNIME. We demonstrate the usefulness of the developed workflow on the basisof two different use cases: a rare disease (here: Glucose Transporter Type 1 (GLUT-1) deficiency), and a new disease (here: COVID 19). The workflow includes a targeteddownload of data through web services, data curation, detection of enriched structural patterns, as well as substructure searches in DrugBank and a recently deposited data set ofantiviral drugs provided by Chemical Abstracts Service. Developed workflows, tutorials with detailed step-by-step instructions, and the information gained by the analysis of data forGLUT-1 deficiency syndrome and COVID-19 are made freely available to the scientific community. The provided framework can be reused by researchers for other in silico drugrepurposing projects, and it should serve as a valuable teaching resource for conveying integrative data mining strategies.Find more about the workflow in our paper:Tuerkova, A., Zdrazil, B. A ligand-based computational drug repurposing pipeline using KNIME and Programmatic Data Access: case studies for rare diseases and COVID-19. JCheminform 12, 71 (2020). https://doi.org/10.1186/s13321-020-00474-z Integrative Data Mining fromChEMBL, PubChem, and IUPHAR Concatenate Column Rename Open Target IDs toUniProt IDs Mapping PDB data retrieval Substructure searches inDrugBank and CAS dataset

Nodes

Extensions

Links

Download

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

Download Workflow
Created by: albik
Created at: 2020-04-22
On NodePit since: 2021-02-26
Last update: 2024-04-18
Created with KNIME version: v4.1.2

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!