scholarly journals A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition

Marine Drugs ◽  
2020 ◽  
Vol 18 (12) ◽  
pp. 633
Author(s):  
Susana P. Gaudêncio ◽  
Florbela Pereira
Keyword(s):  
Virtual Screening ◽  
Drug Design ◽  
Predictive Accuracy ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Classification Model ◽  
Protease Inhibition ◽  
Computer Aided Drug Design ◽  
Main Protease ◽  
Computer Aided

The investigation of marine natural products (MNPs) as key resources for the discovery of drugs to mitigate the COVID-19 pandemic is a developing field. In this work, computer-aided drug design (CADD) approaches comprising ligand- and structure-based methods were explored for predicting SARS-CoV-2 main protease (Mpro) inhibitors. The CADD ligand-based method used a quantitative structure–activity relationship (QSAR) classification model that was built using 5276 organic molecules extracted from the ChEMBL database with SARS-CoV-2 screening data. The best model achieved an overall predictive accuracy of up to 67% for an external and internal validation using test and training sets. Moreover, based on the best QSAR model, a virtual screening campaign was carried out using 11,162 MNPs retrieved from the Reaxys® database, 7 in-house MNPs obtained from marine-derived actinomycetes by the team, and 14 MNPs that are currently in the clinical pipeline. All the MNPs from the virtual screening libraries that were predicted as belonging to class A were selected for the CADD structure-based method. In the CADD structure-based approach, the 494 MNPs selected by the QSAR approach were screened by molecular docking against Mpro enzyme. A list of virtual screening hits comprising fifteen MNPs was assented by establishing several limits in this CADD approach, and five MNPs were proposed as the most promising marine drug-like leads as SARS-CoV-2 Mpro inhibitors, a benzo[f]pyrano[4,3-b]chromene, notoamide I, emindole SB beta-mannoside, and two bromoindole derivatives.

scholarly journals Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis

2021 ◽  
Vol 14 (4) ◽  
pp. 357
Author(s):  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Vijay H. Masand ◽  
Siddhartha Akasapu ◽  
Sumit O. Bajaj ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Virtual Screening ◽  
Simulation Analysis ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Dynamics Simulation ◽  
Multilinear Regression ◽  
Qsar Analysis ◽  
Main Protease

Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure−Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA–MLR (Genetic Algorithm–Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole–indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.

scholarly journals COMPUTER AIDED DRUG DESIGN: AN OVERVIEW

2018 ◽  
Vol 8 (5) ◽  
pp. 504-509 ◽  
Author(s):  
Surabhi Surabhi ◽  
BK Singh
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Drug Design ◽  
Computer Aided Drug Design ◽  
Structure Based Drug Design ◽  
Drug Discovery And Development ◽  
Research Areas ◽  
Computer Aided ◽  
Pharmaceutical Industries

Discovery and development of a new drug is generally known as a very complex process which takes a lot of time and resources. So now a day’s computer aided drug design approaches are used very widely to increase the efficiency of the drug discovery and development course. Various approaches of CADD are evaluated as promising techniques according to their need, in between all these structure-based drug design and ligand-based drug design approaches are known as very efficient and powerful techniques in drug discovery and development. These both methods can be applied with molecular docking to virtual screening for lead identification and optimization. In the recent times computational tools are widely used in pharmaceutical industries and research areas to improve effectiveness and efficacy of drug discovery and development pipeline. In this article we give an overview of computational approaches, which is inventive process of finding novel leads and aid in the process of drug discovery and development research. Keywords: computer aided drug discovery, structure-based drug design, ligand-based drug design, virtual screening and molecular docking

Computer-aided Drug Design and Drug Pharmacokinetic Prediction: A Mini-review

2018 ◽  
Vol 24 (26) ◽  
pp. 3014-3019 ◽  
Author(s):  
Jamshid Tabeshpour ◽  
Amirhossein Sahebkar ◽  
Mohammad Reza Zirak ◽  
Majid Zeinali ◽  
Mahmoud Hashemzaei ◽  
...  
Keyword(s):  
Drug Design ◽  
Target Prediction ◽  
Pharmacokinetic Profile ◽  
Quantitative Structure Activity Relationship ◽  
The Body ◽  
Qsar Modeling ◽  
Computer Aided Drug Design ◽  
Rational Drug ◽  
Computer Aided ◽  
Drug Target Prediction

Prediction of pharmacokinetics and drug targeting is a challenge in drug design. There are different types of software that can help to predict the pharmacokinetic profile of a drug. Quantitative structure-activity relationship (QSAR) modeling is used for drug design with less cost. Drug-excipient interactions are predicted by docking tools. Computerized drug target prediction and docking programs offer additional options to predict potential effects and adverse reactions of a given candidate as well as the best orientation of the compound on the receptor active site. Information on the absorption, distribution, metabolism and excretion of the drug in the body can enhance prediction of drug release and distribution in the blood and central nervous system (CNS). Computer- aided drug design and delivery can help to save the time and cost in the process of rational drug development.

The discovery of new acetylcholinesterase inhibitors derived from pharmacophore modeling, virtual screening, docking simulation and bioassays

2016 ◽  
Vol 12 (12) ◽  
pp. 3734-3742 ◽  
Author(s):  
Yunqin Zhang ◽  
Shuqun Zhang ◽  
Guowei Xu ◽  
Hui Yan ◽  
Yinglan Pu ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Drug Design ◽  
Docking Simulation ◽  
Acetylcholinesterase Inhibitors ◽  
Pharmacophore Modeling ◽  
Computer Aided Drug Design ◽  
Ache Inhibitors ◽  
Computer Aided

Novel AChE inhibitors are discovered using computer aided drug design and bioassays.

Consensus Docking in Drug Discovery

2020 ◽  
Vol 16 (3) ◽  
pp. 182-190 ◽  
Author(s):  
Giulio Poli ◽  
Tiziano Tuccinardi
Keyword(s):  
Virtual Screening ◽  
Drug Design ◽  
Binding Mode ◽  
Point Of View ◽  
Pose Prediction ◽  
Scoring Functions ◽  
Computer Aided Drug Design ◽  
Successful Case ◽  
Computer Aided ◽  
High Level

Background: Molecular docking is probably the most popular and profitable approach in computer-aided drug design, being the staple technique for predicting the binding mode of bioactive compounds and for performing receptor-based virtual screening studies. The growing attention received by docking, as well as the need for improving its reliability in pose prediction and virtual screening performance, has led to the development of a wide plethora of new docking algorithms and scoring functions. Nevertheless, it is unlikely to identify a single procedure outperforming the other ones in terms of reliability and accuracy or demonstrating to be generally suitable for all kinds of protein targets. Methods: In this context, consensus docking approaches are taking hold in computer-aided drug design. These computational protocols consist in docking ligands using multiple docking methods and then comparing the binding poses predicted for the same ligand by the different methods. This analysis is usually carried out calculating the root-mean-square deviation among the different docking results obtained for each ligand, in order to identify the number of docking methods producing the same binding pose. Results: The consensus docking approaches demonstrated to improve the quality of docking and virtual screening results compared to the single docking methods. From a qualitative point of view, the improvement in pose prediction accuracy was obtained by prioritizing ligand binding poses produced by a high number of docking methods, whereas with regards to virtual screening studies, high hit rates were obtained by prioritizing the compounds showing a high level of pose consensus. Conclusion: In this review, we provide an overview of the results obtained from the performance assessment of various consensus docking protocols and we illustrate successful case studies where consensus docking has been applied in virtual screening studies.

Harnessing the Capability of CADD Methods in the Prediction of Anti-COVID Drug Likeliness

2021 ◽  
pp. 269-287
Author(s):  
Shubhra Chaturvedi ◽  
Vishaka Chaudhary ◽  
Tina Klauss ◽  
Philippe Barthélémy ◽  
Anil Kumar Mishra
Keyword(s):  
Psychological Distress ◽  
Virtual Screening ◽  
Drug Design ◽  
Ab Initio ◽  
Social Economic ◽  
Design Methods ◽  
Drug Molecule ◽  
Computer Aided Drug Design ◽  
The Social ◽  
Computer Aided

The COVID-19 pandemic has claimed many lives and added to the social, economic, and psychological distress. The contagious disease has quickly spread to almost 200 countries following the regional outbreak in China. As the number of infected populations increases exponentially, there is a pressing demand for anti-COVID drugs and vaccines. Virtual screening provides possible leads while extensively cutting down the time and resources required for ab-initio drug design. The chapter aims to highlight the various computer-aided drug design methods to predict an anti-COVID drug molecule.

scholarly journals Computer aided drug design using virtual screening and molecular energy calculation of a specific neurodegenerative diseases

2018 ◽  
Vol 7 (1.9) ◽  
pp. 141
Author(s):  
Bipin Nair B J ◽  
Akshay Rajendran
Keyword(s):  
Virtual Screening ◽  
Drug Design ◽  
Neurodegenerative Diseases ◽  
Neurodegenerative Disease ◽  
Data Bank ◽  
Binding Energies ◽  
Energy Calculation ◽  
Computer Aided Drug Design ◽  
Structure Of Molecules ◽  
Computer Aided

Computer-aided drug design (CADD) is designing a drug with the help of computational algorithms. Information technology advances to creates the structure of molecules, molecular modeling and calculate the binding energies of the drug to initiate a new medicine against neurodegenerative diseases. In our work, we implemented virtual screening of a drug-protein interaction is selected from drug data bank with potential drug bank inhibitory activity for a specific neurodegenerative disease. Here we analyze technical CADD studies of the neurodegenerative diseases. Finally selecting the best alkaloid for a specific neurodegenerative disease and predicting the efficiency using computation of alkaloid with molecular energy.

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