Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

2021 ◽  
pp. 113705
Author(s):  
Victor T. Sabe ◽  
Thandokuhle Ntombela ◽  
Lindiwe A. Jhamba ◽  
Glenn E.M. Maguire ◽  
Thavendran Govender ◽  
...  
Keyword(s):  
Drug Design ◽  
Computational Techniques ◽  
Computer Aided Drug Design ◽  
Current Trends ◽  
Computer Aided

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.

scholarly journals A Data Science Approach to Bioinformatics

2021 ◽  
Vol 9 (VII) ◽  
pp. 3860-3869
Author(s):  
Palepu Narasimha Rakesh
Keyword(s):  
Drug Design ◽  
Binding Affinity ◽  
Data Science ◽  
Neural Nets ◽  
Quantitative Prediction ◽  
Computational Techniques ◽  
Computational Technique ◽  
Screening Process ◽  
Computer Aided Drug Design ◽  
Computer Aided

Computer aided drug design (CADD) which uses the computational advance towards to develop, discover and scrutinize and examine drugs and alike biologically agile molecules. CADD is a specialized stream which uses the computational techniques to mimic drug-receptor interactions. CADD procedures are so much dependent on the tools of bioinformatics, databases & applications. There are so many advantages of computer aided drug discovery; it saves lot of time which is one of the main advantages followed by low cost and more accuracy. CADD required less manpower to work. There are different types of CADD such as ligand and structure based design. Objectives of the Computer aided drug design are to boost up the screening process, to test the rational of drug design, to efficiently screen and to remove hopeless ones as early as possible. In Drug designing the selected molecule should be organic small molecule, complementary in shape to the target and oppositely charged to the biomolecular target. The molecule will interacts and binds with the target which activates or inhibits the function of a biomolecule such as a protein or lipid. The main basic goal in the drug design is to forecast whether a given molecule will bind to target and if thus how strongly. Molecular mechanics techniques also used to provide the semi quantitative prediction of the binding affinity. These techniques use machine learning, linear regression, neural nets or other statistical methods to derive predictive binding affinity equations. Preferably, the computational technique will be able to forecast the affinity prior to a compound is synthesized, saving huge time and cost. Computational techniques have quickened the discovery by decreasing the number of iterations required and have often produced the novel structures.

Computer Aided Drug Design Approaches for Identification of Novel Autotaxin (ATX) Inhibitors

2016 ◽  
Vol 23 (17) ◽  
pp. 1708-1724 ◽  
Author(s):  
Eleni Vrontaki ◽  
Georgia Melagraki ◽  
Eleanna Kaffe ◽  
Thomas Mavromoustakos ◽  
George Kokotos ◽  
...  
Keyword(s):  
Drug Design ◽  
Computer Aided Drug Design ◽  
Computer Aided

Prospects for Discovering the Secondary Metabolites of Cordyceps Sensu Lato by the Integrated Strategy

2020 ◽  
Vol 17 (2) ◽  
pp. 97-120
Author(s):  
Shabana Bibi ◽  
Yuan-Bing Wang ◽  
De-Xiang Tang ◽  
Mohammad Amjad Kamal ◽  
Hong Yu
Keyword(s):  
Drug Design ◽  
Biological Activities ◽  
Design Methods ◽  
Chinese Herbs ◽  
Active Metabolites ◽  
Computer Aided Drug Design ◽  
Design Concepts ◽  
Conventional Drug ◽  
Computer Aided ◽  
Design Techniques

: Some species of Cordyceps sensu lato are famous Chinese herbs with significant biological activities, often used as edible food and traditional medicine in China. Cordyceps represents the largest entomopathogenic group of fungi, including 40 genera and 1339 species in three families and incertae sedis of Hypocreales. Objective: Most of the Cordyceps-derivatives have been approved clinically for the treatment of various diseases such as diabetes, cancers, inflammation, cardiovascular, renal and neurological disorders and are used worldwide as supplements and herbal drugs, but there is still need for highly efficient Cordyceps-derived drugs for fatal diseases with approval of the U.S. Food and Drug Administration. Methods: Computer-aided drug design concepts could improve the discovery of putative Cordyceps- derived medicine within less time and low budget. The integration of computer-aided drug design methods with experimental validation has contributed to the successful discovery of novel drugs. Results: This review focused on modern taxonomy, active metabolites, and modern drug design techniques that could accelerate conventional drug design and discovery of Cordyceps s. l. Successful application of computer-aided drug design methods in Cordyceps research has been discussed. Conclusion: It has been concluded that computer-aided drug design techniques could influence the multiple target-focused drug design, because each metabolite of Cordyceps has shown significant activities for the various diseases with very few or no side effects.

Zoning in on Tankyrases: A Brief Review on the Past, Present and Prospective Studies

2020 ◽  
Vol 19 (16) ◽  
pp. 1920-1934
Author(s):  
Xylia Q. Peters ◽  
Thembeka H. Malinga ◽  
Clement Agoni ◽  
Fisayo A. Olotu ◽  
Mahmoud E.S. Soliman
Keyword(s):  
Drug Design ◽  
Small Molecule ◽  
Small Molecule Inhibitors ◽  
Design Methods ◽  
Selective Inhibitors ◽  
Computer Aided Drug Design ◽  
Cellular Processes ◽  
Conventional Drug ◽  
Computer Aided ◽  
Tankyrase 1

Background: Tankyrases are known for their multifunctionalities within the poly(ADPribose) polymerases family and playing vital roles in various cellular processes which include the regulation of tumour suppressors. Tankyrases, which exist in two isoforms; Tankyrase 1 and 2, are highly homologous and an integral part of the Wnt β -catenin pathway that becomes overly dysregulated when hijacked by pro-carcinogenic machineries. Methods: In this review, we cover the distinct roles of the Tankyrase isoforms and their involvement in the disease pathogenesis. Also, we provide updates on experimentally and computationally derived antagonists of Tankyrase whilst highlighting the precedence of integrative computer-aided drug design methods towards the discovery of selective inhibitors. Results: Despite the high prospects embedded in the therapeutic targeting and blockade of Tankyrase isoforms, the inability of small molecule inhibitors to achieve selective targeting has remained a major setback, even until date. This explains numerous incessant drug design efforts geared towards the development of highly selective inhibitors of the respective Tankyrase isoforms since they mediate distinct aberrancies in disease progression. Therefore, considering the setbacks of conventional drug design methods, can computer-aided approaches actually save the day? Conclusion: The implementation of computer-aided drug design techniques in Tankyrase research could help complement experimental methods and facilitate ligand/structure-based design and discovery of small molecule inhibitors with enhanced selectivity.

scholarly journals Advances in Applying Computer-Aided Drug Design for Neurodegenerative Diseases

2021 ◽  
Vol 22 (9) ◽  
pp. 4688
Author(s):  
Mootaz M. Salman ◽  
Zaid Al-Obaidi ◽  
Philip Kitchen ◽  
Andrea Loreto ◽  
Roslyn M. Bill ◽  
...  
Keyword(s):  
Drug Design ◽  
Neurodegenerative Diseases ◽  
Docking Studies ◽  
New Drugs ◽  
Computer Aided Drug Design ◽  
Biological Assays ◽  
Advantages And Disadvantages ◽  
Computer Aided ◽  
Research Challenge ◽  
The Cost

Neurodegenerative diseases (NDs) including Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and Huntington’s disease are incurable and affect millions of people worldwide. The development of treatments for this unmet clinical need is a major global research challenge. Computer-aided drug design (CADD) methods minimize the huge number of ligands that could be screened in biological assays, reducing the cost, time, and effort required to develop new drugs. In this review, we provide an introduction to CADD and examine the progress in applying CADD and other molecular docking studies to NDs. We provide an updated overview of potential therapeutic targets for various NDs and discuss some of the advantages and disadvantages of these tools.

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