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>

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 QPoweredTarget2DeNovoDrugPropMax : a novel programmatic tool incorporating deep learning and in silico methods for automated de novo drug design for any target of interest

2021 ◽  
Author(s):  
Ben Geoffrey ◽  
Rafal Madaj ◽  
Pavan Preetham Valluri ◽  
Akhil Sanker
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Discovery ◽  
In Silico ◽  
Drug Target ◽  
Data Science ◽  
De Novo ◽  
Autodock Vina ◽  
De Novo Drug Design ◽  
Ligand Interaction

The past decade has seen a surge in the range of application data science, machine learning, deep learning, and AI methods to drug discovery. The presented work involves an assemblage of a variety of AI methods for drug discovery along with the incorporation of in silico techniques to provide a holistic tool for automated drug discovery. When drug candidates are required to be identified for aparticular drug target of interest, the user is required to provide the tool target signatures in the form of an amino acid sequence or its corresponding nucleotide sequence. The tool collects data registered on PubChem required to perform an automated QSAR and with the validated QSAR model, prediction and drug lead generation are carried out. This protocol we call Target2Drug. This is followed by a protocol we call Target2DeNovoDrug wherein novel molecules with likely activityagainst the target are generated de novo using a generative LSTM model. It is often required in drug discovery that the generated molecules possess certain properties like drug-likeness, and therefore to optimize the generated de novo molecules toward the required drug-like property we use a deep learning model called DeepFMPO, and this protocol we call Target2DeNovoDrugPropMax. This is followed by the fast automated AutoDock-Vina based in silico modeling and profiling of theinteraction of optimized drug leads and the drug target. This is followed by an automated execution of the Molecular Dynamics protocol that is also carried out for the complex identified with the best protein-ligand interaction from the AutoDock- Vina based virtual screening. The results are stored in the working folder of the user. The code is maintained, supported, and provide for use in thefollowing GitHub repositoryhttps://github.com/bengeof/Target2DeNovoDrugPropMaxAnticipating the rise in the use of quantum computing and quantum machine learning in drug discovery we use the Penny-lane interface to quantum hardware to turn classical Keras layers used in our machine/deep learning models into a quantum layer and introduce quantum layers into our classical models to produce a quantum-classical machine/deep learning hybrid model of our tool and the code corresponding to the same is provided belowhttps://github.com/bengeof/QPoweredTarget2DeNovoDrugPropMax

Towards a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning

2019 ◽  
Author(s):  
Seoin Back ◽  
Kevin Tran ◽  
Zachary Ulissi
Keyword(s):  
Machine Learning ◽  
Oxygen Evolution ◽  
Active Sites ◽  
Density Functional ◽  
Chemical Space ◽  
Density Functional Theory Calculations ◽  
Catalyst Design ◽  
Transition Metal Catalysts ◽  
Oxide Materials ◽  
Design Strategies

<div> <div> <div> <div><p>Developing active and stable oxygen evolution catalysts is a key to enabling various future energy technologies and the state-of-the-art catalyst is Ir-containing oxide materials. Understanding oxygen chemistry on oxide materials is significantly more complicated than studying transition metal catalysts for two reasons: the most stable surface coverage under reaction conditions is extremely important but difficult to understand without many detailed calculations, and there are many possible active sites and configurations on O* or OH* covered surfaces. We have developed an automated and high-throughput approach to solve this problem and predict OER overpotentials for arbitrary oxide surfaces. We demonstrate this for a number of previously-unstudied IrO2 and IrO3 polymorphs and their facets. We discovered that low index surfaces of IrO2 other than rutile (110) are more active than the most stable rutile (110), and we identified promising active sites of IrO2 and IrO3 that outperform rutile (110) by 0.2 V in theoretical overpotential. Based on findings from DFT calculations, we pro- vide catalyst design strategies to improve catalytic activity of Ir based catalysts and demonstrate a machine learning model capable of predicting surface coverages and site activity. This work highlights the importance of investigating unexplored chemical space to design promising catalysts.<br></p></div></div></div></div><div><div><div> </div> </div> </div>

Graphene adlayer growth between nonepitaxial graphene and Ni(111) substrate: a promising theoretical scheme

2020 ◽  
Author(s):  
Lijuan Meng ◽  
Jinlian Lu ◽  
Yujie Bai ◽  
Lili Liu ◽  
Tang Jingyi ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Density Functional Theory ◽  
Chemical Vapor Deposition ◽  
Molecular Dynamics Simulations ◽  
Vapor Deposition ◽  
Density Functional ◽  
Chemical Vapor ◽  
Density Functional Theory Calculations ◽  
Functional Theory ◽  
Dynamics Simulations

Understanding the fundamentals of chemical vapor deposition bilayer graphene growth is crucial for its synthesis. By employing density functional theory calculations and classical molecular dynamics simulations, we have investigated the...

scholarly journals Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2505
Author(s):  
Raheem Remtulla ◽  
Sanjoy Kumar Das ◽  
Leonard A. Levin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
In Silico ◽  
Disulfide Bonds ◽  
Oral Absorption ◽  
In Vivo Studies ◽  
Drug Candidates ◽  
In Silico Methods

Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated in the downstream effects of axonal injury. A difficulty in using standard in silico methods for studying these drugs is that most computational tools are not designed for borane-containing compounds. Using in silico and machine learning methodologies, the absorption-distribution properties of these unique compounds were assessed. Features examined with in silico methods included cellular permeability, octanol-water partition coefficient, blood-brain barrier permeability, oral absorption and serum protein binding. The resultant neural networks demonstrated an appropriate level of accuracy and were comparable to existing in silico methodologies. Specifically, they were able to reliably predict pharmacokinetic features of known boron-containing compounds. These methods predicted that phosphine-borane compounds and their metabolites meet the necessary pharmacokinetic features for orally active drug candidates. This study showed that the combination of standard in silico predictive and machine learning models with neural networks is effective in predicting pharmacokinetic features of novel boron-containing compounds as neuroprotective drugs.

scholarly journals Molecular Insights into Cage Occupancy of Hydrogen Hydrate: A Computational Study

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 699 ◽  
Author(s):  
Ma ◽  
Zhong ◽  
Liu ◽  
Zhong ◽  
Yan ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Hydrogen Storage ◽  
Molecular Dynamics Simulations ◽  
Density Functional ◽  
Storage Capacity ◽  
Computational Study ◽  
Density Functional Theory Calculations ◽  
Hydrogen Storage Capacity ◽  
Large Cage ◽  
Dynamics Simulations

Density functional theory calculations and molecular dynamics simulations were performed to investigate the hydrogen storage capacity in the sII hydrate. Calculation results show that the optimum hydrogen storage capacity is ~5.6 wt%, with the double occupancy in the small cage and quintuple occupancy in the large cage. Molecular dynamics simulations indicate that these multiple occupied hydrogen hydrates can occur at mild conditions, and their stability will be further enhanced by increasing the pressure or decreasing the temperature. Our work highlights that the hydrate is a promising material for storing hydrogen.

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