Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches

Cardiovascular diseases (CDs) are a major concern in the human race and one of the leading causes of death worldwide. β-Adrenergic receptors (β1-AR and β2-AR) play a crucial role in the overall regulation of cardiac function. In the present study, structure-based virtual screening, machine learning (ML), and a ligand-based similarity search were conducted for the PubChem database against both β1- and β2-AR. Initially, all docked molecules were screened using the threshold binding energy value. Molecules with a better binding affinity were further used for segregation as active and inactive through ML. The pharmacokinetic assessment was carried out on molecules retained in the above step. Further, similarity searching of the ChEMBL and DrugBank databases was performed. From detailed analysis of the above data, four compounds for each of β1- and β2-AR were found to be promising in nature. A number of critical ligand-binding amino acids formed potential hydrogen bonds and hydrophobic interactions. Finally, a molecular dynamics (MD) simulation study of each molecule bound with the respective target was performed. A number of parameters obtained from the MD simulation trajectories were calculated and substantiated the stability between the protein-ligand complex. Hence, it can be postulated that the final molecules might be crucial for CDs subjected to experimental validation.

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Diagnostics of the 3D printing process on a CNC machine using machine learning approaches

10.36652/0042-4633-2021-1-55-59 ◽

2021 ◽

pp. 55-59

Author(s):

Yu.G. Kabaldin ◽

D.A. Shatagin ◽

M.S. Anosov ◽

P.V. Kolchin ◽

A.V. Kiselev

Keyword(s):

Nonlinear Dynamics ◽

Machine Learning ◽

3D Printing ◽

Electric Arc ◽

Cyber Physical System ◽

Learning Approaches ◽

Cnc Machine ◽

Printing Process ◽

Optimization Of Process Parameters ◽

The Stability

Diagnostics and optimization of the dynamics of an electric arc during 3D printing on a CNC machine are considered. The application of nonlinear dynamics methods in assessing the stability of the 3D printing process and the use of artificial neural networks in the classification and optimization of process parameters are shown. Keywords: 3D printing, cyber physical system, machine learning, hybrid processing, neuroform controller, diagnostics, digital twin. [email protected]

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Pharmacoinformatics-based identification of transmembrane protease serine-2 inhibitors from Morus Alba as SARS-CoV-2 cell entry inhibitors

Molecular Diversity ◽

10.1007/s11030-021-10209-3 ◽

2021 ◽

Author(s):

Anshul Shakya ◽

Rupesh V. Chikhale ◽

Hans Raj Bhat ◽

Fatmah Ali Alasmary ◽

Tahani Mazyad Almutairi ◽

...

Keyword(s):

Md Simulation ◽

Hydrophobic Interactions ◽

Binding Free Energy ◽

Surface Protein ◽

Morus Alba ◽

Bioactive Molecules ◽

Chemical Components ◽

Pharmacokinetic Parameters ◽

The Stability ◽

Novel Coronavirus

Abstract Transmembrane protease serine-2 (TMPRSS2) is a cell-surface protein expressed by epithelial cells of specific tissues including those in the aerodigestive tract. It helps the entry of novel coronavirus (n-CoV) or Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in the host cell. Successful inhibition of the TMPRSS2 can be one of the crucial strategies to stop the SARS-CoV-2 infection. In the present study, a set of bioactive molecules from Morus alba Linn. were screened against the TMPRSS2 through two widely used molecular docking engines such as Autodock vina and Glide. Molecules having a higher binding affinity toward the TMPRSS2 compared to Camostat and Ambroxol were considered for in-silico pharmacokinetic analyses. Based on acceptable pharmacokinetic parameters and drug-likeness, finally, five molecules were found to be important for the TMPRSS2 inhibition. A number of bonding interactions in terms of hydrogen bond and hydrophobic interactions were observed between the proposed molecules and ligand-interacting amino acids of the TMPRSS2. The dynamic behavior and stability of best-docked complex between TRMPRSS2 and proposed molecules were assessed through molecular dynamics (MD) simulation. Several parameters from MD simulation have suggested the stability between the protein and ligands. Binding free energy of each molecule calculated through MM-GBSA approach from the MD simulation trajectory suggested strong affection toward the TMPRSS2. Hence, proposed molecules might be crucial chemical components for the TMPRSS2 inhibition. Graphic abstract

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Integration of System Biology Tools to Investigate Huperzine A as an Anti-Alzheimer Agent

Frontiers in Pharmacology ◽

10.3389/fphar.2021.785964 ◽

2021 ◽

Vol 12 ◽

Author(s):

Pukar Khanal ◽

Farshid Zargari ◽

Bahareh Farasati Far ◽

Dharmendra Kumar ◽

Mogana R ◽

...

Keyword(s):

System Biology ◽

Hydrophobic Interactions ◽

Huperzine A ◽

Biological Processes ◽

Pubchem Database ◽

Single Compound ◽

Minimum Probability ◽

The Stability ◽

And Function ◽

Cellular Components

Aim: The present study aimed to investigate huperzine A as an anti-Alzheimer agent based on the principle that a single compound can regulate multiple proteins and associated pathways, using system biology tools.Methodology: The simplified molecular-input line-entry system of huperzine A was retrieved from the PubChem database, and its targets were predicted using SwissTargetPrediction. These targets were matched with the proteins deposited in DisGeNET for Alzheimer disease and enriched in STRING to identify the probably regulated pathways, cellular components, biological processes, and molecular function. Furthermore, huperzine A was docked against acetylcholinesterase using AutoDock Vina, and simulations were performed with the Gromacs package to take into account the dynamics of the system and its effect on the stability and function of the ligands.Results: A total of 100 targets were predicted to be targeted by huperzine A, of which 42 were regulated at a minimum probability of 0.05. Similarly, 101 Kyoto Encyclopedia of Genes and Genomes pathways were triggered, in which neuroactive ligand–receptor interactions scored the least false discovery rate. Also, huperzine A was predicted to modulate 54 cellular components, 120 molecular functions, and 873 biological processes. Furthermore, huperzine A possessed a binding affinity of −8.7 kcal/mol with AChE and interacted within the active site of AChE via H-bonds and hydrophobic interactions.

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Deep Learning-Based Election Results Prediction Using Twitter Activity

10.21203/rs.3.rs-839553/v1 ◽

2021 ◽

Author(s):

Haider Ali ◽

Haleem Farman ◽

Hikmat Yar ◽

Zahid Khan ◽

Shabana Habib ◽

...

Keyword(s):

Machine Learning ◽

Social Media ◽

Sentiment Analysis ◽

General Election ◽

Learning Approaches ◽

Social Media Platforms ◽

General Elections ◽

Election Results ◽

Twitter Activity ◽

The Stability

Abstract Nowadays, political parties have widely adopted social media for their party promotions and election campaigns. During the election, Twitter and other social media platforms are used for political coverage to promote the party and its candidates. This research discusses and estimates the stability of many volumetric social media approaches to forecast election results from social media activities. Numerous machine learning approaches are applied to opinions shared on social media for predicting election results. This paper presents a machine learning model based on sentiment analysis to predict Pakistan's general election results. In a general election, voters vote for their favorite party or candidate based on their personal interests. Social media has been extensively used for the campaign in Pakistan general election 2018. Using a machine learning technique, we provide a five-step process to analyze the overall election results, whether fair or unfair. The work is concluded with detailed experimental results and a discussion on the outcomes of sentiment analysis for real-world forecasting and approval for general elections in Pakistan.

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High-Throughput Study of Antisolvents on the Stability of Multicomponent Metal Halide Perovskites through Robotics-Based Synthesis and Machine Learning Approaches

Journal of the American Chemical Society ◽

10.1021/jacs.1c10045 ◽

2021 ◽

Author(s):

Kate Higgins ◽

Maxim Ziatdinov ◽

Sergei V. Kalinin ◽

Mahshid Ahmadi

Keyword(s):

Machine Learning ◽

High Throughput ◽

Metal Halide ◽

Learning Approaches ◽

Halide Perovskites ◽

The Stability ◽

High Throughput Study

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Supplemental Material for Psychometric and Machine Learning Approaches for Diagnostic Assessment and Tests of Individual Classification

Psychological Methods ◽

10.1037/met0000317.supp ◽

2020 ◽

Keyword(s):

Machine Learning ◽

Diagnostic Assessment ◽

Learning Approaches

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Machine Learning Approaches for the Analysis of Non-Metallic Inclusion Data Sets

AISTech2019 Proceedings of the Iron and Steel Technology Conference ◽

10.33313/377/275 ◽

2019 ◽

Author(s):

M. Webler ◽

B. Abdulsalam

Keyword(s):

Machine Learning ◽

Data Sets ◽

Learning Approaches ◽

Metallic Inclusion

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Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

Transport and Communication Science Journal ◽

10.25073/tcsj.70.3.7 ◽

2019 ◽

Vol 70 (3) ◽

pp. 214-224

Author(s):

Bui Ngoc Dung ◽

Manh Dzung Lai ◽

Tran Vu Hieu ◽

Nguyen Binh T. H.

Keyword(s):

Machine Learning ◽

Gaussian Mixture ◽

Research Field ◽

Transport Systems ◽

Learning Approaches ◽

Subtraction Method ◽

Intelligent Transport Systems ◽

Intelligent Transport ◽

Detection And Tracking ◽

Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

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Scalable Approach to High Coverages on Oxides via Iterative Training of a Machine-Learning Algorithm

10.26434/chemrxiv.10288514.v1 ◽

2019 ◽

Author(s):

Andrew Medford ◽

Shengchun Yang ◽

Fuzhu Liu

Keyword(s):

Machine Learning ◽

Chemical Potential ◽

Learning Algorithm ◽

Absolute Error ◽

Low Energy ◽

Training Data ◽

High Coverage ◽

Metal Compounds ◽

Adsorption Energies ◽

The Stability

Understanding the interaction of multiple types of adsorbate molecules on solid surfaces is crucial to establishing the stability of catalysts under various chemical environments. Computational studies on the high coverage and mixed coverages of reaction intermediates are still challenging, especially for transition-metal compounds. In this work, we present a framework to predict differential adsorption energies and identify low-energy structures under high- and mixed-adsorbate coverages on oxide materials. The approach uses Gaussian process machine-learning models with quantified uncertainty in conjunction with an iterative training algorithm to actively identify the training set. The framework is demonstrated for the mixed adsorption of CHx, NHx and OHx species on the oxygen vacancy and pristine rutile TiO2(110) surface sites. The results indicate that the proposed algorithm is highly efficient at identifying the most valuable training data, and is able to predict differential adsorption energies with a mean absolute error of ~0.3 eV based on <25% of the total DFT data. The algorithm is also used to identify 76% of the low-energy structures based on <30% of the total DFT data, enabling construction of surface phase diagrams that account for high and mixed coverage as a function of the chemical potential of C, H, O, and N. Furthermore, the computational scaling indicates the algorithm scales nearly linearly (N1.12) as the number of adsorbates increases. This framework can be directly extended to metals, metal oxides, and other materials, providing a practical route toward the investigation of the behavior of catalysts under high-coverage conditions.

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In Silico Molecular Docking and Molecular Dynamic Simulation of Potential Inhibitors of 3C-like Main Proteinase (3CLpro) from Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) Using Selected African Medicinal Plants

10.26434/chemrxiv.12480434.v1 ◽

2020 ◽

Author(s):

Sahar Qazi ◽

Mustafa Alhaji Isa ◽

Adam Mustapha ◽

Khalid Raza ◽

Ibrahim Alkali Allamin ◽

...

Keyword(s):

Molecular Docking ◽

Severe Acute Respiratory Syndrome ◽

In Silico ◽

Md Simulation ◽

Binding Energies ◽

Anacardium Occidentale ◽

Upper Respiratory Tract ◽

Ligand Complex ◽

In Silico Docking ◽

Main Protease

The Severe Acute Respiratory Syndrome 2 (SARS-CoV-2) is an infectious virus that causes mild to severe life-threatening upper respiratory tract infection. The virus emerged in Wuhan, China in 2019, and later spread across the globe. Its genome has been completely sequenced and based on the genomic information, the virus possessed 3C-Like Main Protease (3CLpro), an essential multifunctional enzyme that plays a vital role in the replication and transcription of the virus by cleaving polyprotein at eleven various sites to produce different non-structural proteins. This makes the protein an important target for drug design and discovery. Herein, we analyzed the interaction between the 3CLpro and potential inhibitory compounds identified from the extracts of Zingiber offinale and Anacardium occidentale using in silico docking and Molecular Dynamics (MD) Simulation. The crystal structure of SARS-CoV-2 main protease in complex with 02J (5-Methylisoxazole-3-carboxylic acid) and PEJ (composite ligand) (PDB Code: 6LU7,2.16Å) retrieved from Protein Data Bank (PDB) and subject to structure optimization and energy minimization. A total of twenty-nine compounds were obtained from the extracts of Zingiber offinale and the leaves of Anacardium occidentale. These compounds were screened for physicochemical (Lipinski rule of five, Veber rule, and Egan filter), Pan-Assay Interference Structure (PAINS), and pharmacokinetic properties to determine the Pharmaceutical Active Ingredients (PAIs). Of the 29 compounds, only nineteen (19) possessed drug-likeness properties with efficient oral bioavailability and less toxicity. These compounds subjected to molecular docking analysis to determine their binding energies with the 3CLpro. The result of the analysis indicated that the free binding energies of the compounds ranged between ˗5.08 and -10.24kcal/mol, better than the binding energies of 02j (-4.10kcal/mol) and PJE (-5.07kcal.mol). Six compounds (CID_99615 = -10.24kcal/mol, CID_3981360 = 9.75kcal/mol, CID_9910474 = -9.14kcal/mol, CID_11697907 = -9.10kcal/mol, CID_10503282 = -9.09kcal/mol and CID_620012 = -8.53kcal/mol) with good binding energies further selected and subjected to MD Simulation to determine the stability of the protein-ligand complex. The results of the analysis indicated that all the ligands form stable complexes with the protein, although, CID_9910474 and CID_10503282 had a better stability when compared to other selected phytochemicals (CID_99615, CID_3981360, CID_620012, and CID_11697907).

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