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bioRxiv · 10.1101/2020.07.07.192518

Automated identification of small drug molecules for Hepatitis C virus through a novel programmatic tool and extensive Molecular Dynamics studies of select drug candidates

Rafal Madaj·Akhil Sanker·Ben Geoffrey A S·Host Antony David·Shubham Verma·Judith Gracia·Ayodele Ifeoluwa Faleti·Abdulbasit Haliru Yakubu
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Abstract

We report a novel python based programmatic tool that automates the dry lab drug discovery workflow for Hepatitis C virus. 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 Hepatitis C virus is generated. 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. 50 of the drug leads generated by the program are fed as programmatic inputs to an In Silico modelling package by the program for fast virtual screening and computer modelling of the interaction of the compounds generated as drug leads and the drug target, a viral Helicase of Hepatitis C. The results are stored automatically in the working folder of the user by the program. The program also generates protein-ligand interaction profiling and stores the visualized images in the working folder of the user. Select protein-ligand complexes associated with structurally diverse ligands having lowest binding energy were selected for extensive molecular dynamics simulation studies and subsequently for molecular mechanics generalized-born surface area (MMGBSA) with pairwise decomposition calculations. The molecular mechanics studies predict In Silico that the compounds generated by the program inhibit the viral helicase of Hepatitis C and prevent the replication of the virus. Thus our programmatic tool ushers in the new age of automatic ease in drug identification for Hepatitis C virus through a programmatic tool that completely automates the dry lab drug discovery workflow. The program is hosted, maintained and supported at the GitHub repository link given below https://github.com/bengeof/Automated-drug-identification-programmatic-tool-for-Hepatitis-C-virusCompeting Interest StatementThe authors have declared no competing interest.View Full Text

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Rafal Madaj, Akhil Sanker, Ben Geoffrey A S, Host Antony David, Shubham Verma, Judith Gracia, Ayodele Ifeoluwa Faleti, Abdulbasit Haliru Yakubu. 2020-07-08. Automated identification of small drug molecules for Hepatitis C virus through a novel programmatic tool and extensive Molecular Dynamics studies of select drug candidates. https://doi.org/10.1101/2020.07.07.192518

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