A programmatic tool for automatic ease in coronavirus drug discovery through programmatically automated data mining, QSAR and In Silico modelling

The work is composed of python based programmatic tool that automates the workflow of drug discovery for coronavirus. Firstly, the python program is written to automate the process of data mining PubChem database to collect data required to perform a machine learning based AutoQSAR algorithm through which drug leads for coronavirus are generated. The data acquisition from PubChem was carried out through python web scrapping techniques. The workflow of the machine learning based AutoQSAR involves feature learning and descriptor selection, QSAR modelling, validation and prediction. The drug leads generated by the program are required to satisfy the Lipinski’s drug likeness criteria as compounds that satisfy Lipinski’s criteria are likely to be an orally active drug in humans. Drug leads generated by the program are fed as programmatic inputs to an In Silico modelling package to computer model the interaction of the compounds generated as drug leads and two coronavirus drug targets identified with their PDB ID : 6W9C and 1P9U. The results are stored in the working folder of the user. The program also generates protein-ligand interaction profiling and stores the visualized images in the working folder of the user. Thus our programmatic tool ushers in the new age automatic ease in drug identification for coronavirus through a fully automated QSAR and an automated In Silico modelling of the drug leads generated by the autoQSAR algorithm.

The program is hosted, maintained and supported at the GitHub repository link given below

https://github.com/bengeof/Programmatic-tool-to-automate-the-drug-discovery-workflow-for-coronavirus