COVID-19 DRUG DOCK KG: A python program for automating the docking results to create a Knowledge Graph
Palak Bhardwaj, Kamal Rawal, Nevidita Arambam, Trapti Sharma · 2022
The recent SARS-CoV-2 epidemic has brought to light significant concerns regarding the dangers of new infectious viruses. having limited time for the creation of new medications. In the clinical domain, the outbreak presents difficulties for therapeutic pharmacological therapy. While there are numerous clinical studies being conducted to treat Covid-19, the approach of molecular docking is commonly being employed for drug screening. The use of molecular docking in drug repurposing techniques shows significant possibilities. The application of docking enables the identification of new pharmaceutical compounds, the molecular prediction of ligand-target interactions, and the understanding of structure-activity linkages. The provided script enabled the creation of an independent docking module which offers a method to automate the docking results and enable the highest binding affinity with 3D models based on the drug-target interactions. Here, we present a python script, cli.py, that efficiently represents a knowledge graph based on docking of the drug targets, AAK1, GAK and JAK1/2.