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

Abstract

<div><p>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.<br><br></p><p>The program is hosted, maintained and supported at the GitHub repository link given below</p><p><a href="https://github.com/bengeof/Programmatic-tool-to-automate-the-drug-discovery-workflow-for-coronavirus">https://github.com/bengeof/Programmatic-tool-to-automate-the-drug-discovery-workflow-for-coronavirus</a></p></div>

QPowered Compound2DeNovoDrugPropMax –A Novel Programmatic Tool Incorporating Deep Learning and In Silico Methods for Automated In Silico Bio- Activity Discovery for any Compound of Interest

“…Similarly advances in understanding differential gene expression from gene expression networks have also been carried out using Deep Learning techniques by different groups [19,20]. The previous works of our research group have involved incorporating machine/deep learning techniques for automation in screening PubChem compound library and identifying the best small drug molecules for a particular drug target [21][22][23]. In keeping with our research focus, the present work presents a complementary approach to drug screening, wherein, given a particular PubChem compound ID for a particular compound, the developed tool predicts the most likely pharmaceutical activity of the compound and followingly performs an automated In Silico modelling to uncover the molecular details of its pharmaceutical activity.…”