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.

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

scholarly journals Exploring Different Virtual Screening Strategies for Acetylcholinesterase Inhibitors

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Nibha Mishra ◽  
Arijit Basu
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Similarity Search ◽  
Acetylcholinesterase Inhibitors ◽  
Enrichment Factors ◽  
Pharmacophore Modeling ◽  
Receiver Operating Curve ◽  
Ache Inhibitors ◽  
Docking Protocol ◽  
Screening Strategies

The virtual screening problems associated with acetylcholinesterase (AChE) inhibitors were explored using multiple shape, and structure-based modeling strategies. The employed strategies include molecular docking, similarity search, and pharmacophore modeling. A subset from directory of useful decoys (DUD) related to AChE inhibitors was considered, which consists of 105 known inhibitors and 3732 decoys. Statistical quality of the models was evaluated by enrichment factor (EF) metrics and receiver operating curve (ROC) analysis. The results revealed that electrostatic similarity search protocol using EON (ET_combo) outperformed all other protocols with outstanding enrichment of>95% in top 1% and 2% of the dataset with an AUC of 0.958. Satisfactory performance was also observed for shape-based similarity search protocol using ROCS and PHASE. In contrast, the molecular docking protocol performed poorly with enrichment factors<30% in all cases. The shape- and electrostatic-based similarity search protocol emerged as a plausible solution for virtual screening of AChE inhibitors.

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 An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Arun Bahadur Gurung ◽  
Mohammad Ajmal Ali ◽  
Joongku Lee ◽  
Mohammad Abul Farah ◽  
Khalid Mashay Al-Anazi
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Drug Targets ◽  
Pharmacophore Modeling ◽  
Review Article ◽  
Drug Candidate ◽  
Computational Techniques ◽  
Computer Aided Drug Design ◽  
Structure Based Drug Design ◽  
Computer Aided

The recent outbreak of the deadly coronavirus disease 19 (COVID-19) pandemic poses serious health concerns around the world. The lack of approved drugs or vaccines continues to be a challenge and further necessitates the discovery of new therapeutic molecules. Computer-aided drug design has helped to expedite the drug discovery and development process by minimizing the cost and time. In this review article, we highlight two important categories of computer-aided drug design (CADD), viz., the ligand-based as well as structured-based drug discovery. Various molecular modeling techniques involved in structure-based drug design are molecular docking and molecular dynamic simulation, whereas ligand-based drug design includes pharmacophore modeling, quantitative structure-activity relationship (QSARs), and artificial intelligence (AI). We have briefly discussed the significance of computer-aided drug design in the context of COVID-19 and how the researchers continue to rely on these computational techniques in the rapid identification of promising drug candidate molecules against various drug targets implicated in the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The structural elucidation of pharmacological drug targets and the discovery of preclinical drug candidate molecules have accelerated both structure-based as well as ligand-based drug design. This review article will help the clinicians and researchers to exploit the immense potential of computer-aided drug design in designing and identification of drug molecules and thereby helping in the management of fatal disease.

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.

scholarly journals DrugOn: a fully integrated pharmacophore modeling and structure optimization toolkit

2014 ◽  
Author(s):  
Dimitrios Vlachakis ◽  
Paraskevas Fakourelis ◽  
Christos Makris ◽  
Sophia Kossida
Keyword(s):  
Drug Design ◽  
Molecular System ◽  
Pharmacophore Modeling ◽  
Command Line ◽  
Computer Aided Drug Design ◽  
Fully Integrated ◽  
The Past ◽  
Modern Drug ◽  
Computer Aided ◽  
Server System

During the past years pharmacophore modeling has become one of the key components in computer-aided drug design and generally in modern drug discovery. DrugOn is a fully interactive pipeline designed to exploit the advantages of modern programming and overcome the command line barrier with two friendly environments for the user (either novice or experienced in the field of Computer Aided Drug Design) to perform pharmacophore modeling through an efficient combination of the PharmACOphore, Gromacs, Ligbuilder and PDB2PQR suites. Our platform features a novel workflow that guides the user through each logical step of the iterative 3D structural optimization setup and drug design process. For the pharmacophore modeling we are focusing on either the characteristics of the receptor or the full molecular system, including a set of selected ligands. DrugOn can be freely downloaded from our dedicated server system at www.bioacademy.gr/bioinformatics/drugon/

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