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

2020 ◽  
Author(s):  
Rafal Madaj ◽  
Akhil Sanker ◽  
Ben Geoffrey A S ◽  
Host Antony David ◽  
Shubham Verma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Drug Discovery ◽  
Molecular Mechanics ◽  
In Silico ◽  
Drug Identification ◽  
Drug Leads ◽  
Viral Helicase

AbstractWe 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-virus

scholarly journals Automated In Silico Identification of Drug Candidates for Coronavirus Through a Novel Programmatic Tool and Extensive Computational (MD, DFT) Studies of Select Drug Candidates

2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Rafal Madaj ◽  
Akhil Sanker ◽  
Mario Sergio Valdés Tresanco ◽  
Host Antony Davidd ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Drug Discovery ◽  
In Silico ◽  
Density Functional ◽  
Density Functional Theory Calculations ◽  
Ligand Interaction ◽  
Drug Candidates ◽  
Descriptor Selection ◽  
Drug Leads

<p>The work is composed of python based programmatic tool that automates the dry lab drug discovery workflow 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 the coronaviral drug target identified with their PDB ID : 6Y84. 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. Select drug leads were further studied extensively using Molecular Dynamics Simulations and best binders and their reactive profiles were analysed using Molecular Dynamics and Density Functional Theory calculations. Thus our programmatic tool ushers in a new age of automatic ease in drug identification for coronavirus. </p><p><br></p><p><br></p><p>The program is hosted, maintained and supported at the GitHub repository link given below</p><p><br></p><p>https://github.com/bengeof/Programmatic-tool-to-automate-the-drug-discovery-workflow-for-coronavirus</p>

scholarly journals Automated In Silico Identification of Drug Candidates for Coronavirus Through a Novel Programmatic Tool and Extensive Computational (MD, DFT) Studies of Select Drug Candidates

2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Rafal Madaj ◽  
Akhil Sanker ◽  
Mario Sergio Valdés Tresanco ◽  
Host Antony Davidd ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Drug Discovery ◽  
In Silico ◽  
Density Functional ◽  
Density Functional Theory Calculations ◽  
Ligand Interaction ◽  
Drug Candidates ◽  
Descriptor Selection ◽  
Drug Leads

<p>The work is composed of python based programmatic tool that automates the dry lab drug discovery workflow 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 the coronaviral drug target identified with their PDB ID : 6Y84. 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. Select drug leads were further studied extensively using Molecular Dynamics Simulations and best binders and their reactive profiles were analysed using Molecular Dynamics and Density Functional Theory calculations. Thus our programmatic tool ushers in a new age of automatic ease in drug identification for coronavirus. </p><p><br></p><p><br></p><p>The program is hosted, maintained and supported at the GitHub repository link given below</p><p><br></p><p>https://github.com/bengeof/Programmatic-tool-to-automate-the-drug-discovery-workflow-for-coronavirus</p>

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

2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Rafal Madaj ◽  
Akhil Sanker ◽  
Mario Sergio Valdés Tresanco ◽  
Host Antony Davidd ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Drug Discovery ◽  
In Silico ◽  
Drug Targets ◽  
Feature Learning ◽  
Ligand Interaction ◽  
Descriptor Selection ◽  
In Silico Modelling ◽  
Drug Leads

<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>

Current Drug Discovery for Anti-hepatitis C Virus Targeting NS4B

2016 ◽  
Vol 16 (12) ◽  
pp. 1362-1371 ◽  
Author(s):  
Zhenya Wang ◽  
Xinli Chen ◽  
Chunli Wu ◽  
Haiwei Xu ◽  
Hongmin Liu
Keyword(s):  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Drug Discovery ◽  
Current Drug

scholarly journals Ketoamide Resistance and Hepatitis C Virus Fitness in Val55 Variants of the NS3 Serine Protease

2012 ◽  
Vol 56 (4) ◽  
pp. 1907-1915 ◽  
Author(s):  
Christoph Welsch ◽  
Sabine Schweizer ◽  
Tetsuro Shimakami ◽  
Francisco S. Domingues ◽  
Seungtaek Kim ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Rna Replication ◽  
Viral Fitness ◽  
Dynamics Simulation ◽  
Structural Flexibility ◽  
Residue Interaction

ABSTRACTDrug-resistant viral variants are a major issue in the use of direct-acting antiviral agents in chronic hepatitis C. Ketoamides are potent inhibitors of the NS3 protease, with V55A identified as mutation associated with resistance to boceprevir. Underlying molecular mechanisms are only partially understood. We applied a comprehensive sequence analysis to characterize the natural variability at Val55 within dominant worldwide patient strains. A residue-interaction network and molecular dynamics simulation were applied to identify mechanisms for ketoamide resistance and viral fitness in Val55 variants. An infectious H77S.3 cell culture system was used for variant phenotype characterization. We measured antiviral 50% effective concentration (EC50) and fold changes, as well as RNA replication and infectious virus yields from viral RNAs containing variants. Val55 was found highly conserved throughout all hepatitis C virus (HCV) genotypes. The conservative V55A and V55I variants were identified from HCV genotype 1a strains with no variants in genotype 1b. Topology measures from a residue-interaction network of the protease structure suggest a potential Val55 key role for modulation of molecular changes in the protease ligand-binding site. Molecular dynamics showed variants with constricted binding pockets and a loss of H-bonded interactions upon boceprevir binding to the variant proteases. These effects might explain low-level boceprevir resistance in the V55A variant, as well as the Val55 variant, reduced RNA replication capacity. Higher structural flexibility was found in the wild-type protease, whereas variants showed lower flexibility. Reduced structural flexibility could impact the Val55 variant's ability to adapt for NS3 domain-domain interaction and might explain the virus yield drop observed in variant strains.

scholarly journals Providing the ‘Best’ Lipophilicity Assessment in a Drug Discovery Environment

2021 ◽  
Author(s):  
george chang ◽  
Nathaniel Woody ◽  
Christopher Keefer
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
High Throughput ◽  
In Silico ◽  
Shake Flask ◽  
Chromatographic Method ◽  
Learning Approach ◽  
Rule Based ◽  
Machine Learning Approach ◽  
High Throughput Screens

Lipophilicity is a fundamental structural property that influences almost every aspect of drug discovery. Within Pfizer, we have two complementary high-throughput screens for measuring lipophilicity as a distribution coefficient (LogD) – a miniaturized shake-flask method (SFLogD) and a chromatographic method (ELogD). The results from these two assays are not the same (see Figure 1), with each assay being applicable or more reliable in particular chemical spaces. In addition to LogD assays, the ability to predict the LogD value for virtual compounds is equally vital. Here we present an in-silico LogD model, applicable to all chemical spaces, based on the integration of the LogD data from both assays. We developed two approaches towards a single LogD model – a Rule-based and a Machine Learning approach. Ultimately, the Machine Learning LogD model was found to be superior to both internally developed and commercial LogD models.<br>

scholarly journals A Program for Automated Data Mining of PubChem to Screen a Billion Compounds and Generate by Machine Learning Based AutoQSAR Algorithm Anti-Corona Viral Drug Leads (Replicase Polyprotein 1ab Inhibitors) and in Silico Study of the Top Drug Lead Compounds

2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Akhil Sanker ◽  
Host Antony Davidd ◽  
Judith Gracia
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
In Silico ◽  
Drug Target ◽  
Data Set ◽  
Lead Compounds ◽  
Pubchem Database ◽  
Drug Lead ◽  
Lead Generation ◽  
Drug Leads

Our work is composed of a python program for automatic data mining of PubChem database to collect data associated with the corona virus drug target replicase polyprotein 1ab (UniProt identifier : POC6X7 ) of data set involving active compounds, their activity value (IC50) and their chemical/molecular descriptors to run a machine learning based AutoQSAR algorithm on the data set to generate anti-corona viral drug leads. The machine learning based AutoQSAR algorithm involves feature selection, QSAR modelling, validation and prediction. The drug leads generated each time the program is run is reflective of the constantly growing PubChem database is an important dynamic feature of the program which facilitates fast and dynamic anti-corona viral drug lead generation reflective of the constantly growing PubChem database. The program prints out the top anti-corona viral drug leads after screening PubChem library which is over a billion compounds. The interaction of top drug lead compounds generated by the program and two corona viral drug target proteins, 3-Cystiene like Protease (3CLPro) and Papain like protease (PLpro) was studied and analysed using molecular docking tools. The compounds generated as drug leads by the program showed favourable interaction with the drug target proteins and thus we recommend the program for use in anti-corona viral compound drug lead generation as it helps reduce the complexity of virtual screening and ushers in an age of automatic ease in drug lead generation. The leads generated by the program can further be tested for drug potential through further In Silico, In Vitro and In Vivo testing <div><br></div><div><div>The program is hosted, maintained and supported at the GitHub repository link given below</div><div><br></div><div>https://github.com/bengeof/Drug-Discovery-P0C6X7</div></div><div><br></div>

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