scholarly journals Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

2021 ◽  
Vol 22 (24) ◽  
pp. 13259
Author(s):  
Murtala A. Ejalonibu ◽  
Segun A. Ogundare ◽  
Ahmed A. Elrashedy ◽  
Morufat A. Ejalonibu ◽  
Monsurat M. Lawal ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Design ◽  
Computational Techniques ◽  
Drug Management ◽  
New Techniques ◽  
Computer Aided Drug Design ◽  
Resistant Tuberculosis ◽  
Traditional Drug ◽  
New Generation ◽  
Insight Into

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

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.

High-pressure X-ray science on the ultimate storage ring

2014 ◽  
Vol 21 (5) ◽  
pp. 1077-1083 ◽  
Author(s):  
Malcolm I. McMahon
Keyword(s):  
High Pressure ◽  
High Energy ◽  
Storage Rings ◽  
Nuclear Scattering ◽  
New Techniques ◽  
X Ray ◽  
The Earth ◽  
New Generation ◽  
First Time ◽  
Insight Into

The advent of the ESRF, APS and SPring-8 third-generation synchrotron sources in the mid-1990s heralded a golden age of high-pressure X-ray science. The high-energy monochromatic micro-focused X-ray beams from these storage rings, combined with the new high-pressure diffraction and spectroscopy techniques developed in the late 1980s, meant that researchers were immediately able to make detailed structural studies at pressures comparable with those at the centre of the Earth, studies that were simply not possible only five years previously. And new techniques, such as X-ray inelastic scattering and X-ray nuclear scattering, became possible at high pressure for the first time, providing wholly-new insight into the behaviour of materials at high densities. The arrival of new diffraction-limited storage rings, with their much greater brightness, and ability to achieve focal-spot diameters for high-energy X-ray beams of below 1 µm, offers the possibility of a new generation of high-pressure science, both extending the scope of what is already possible, and also opening ways to wholly-new areas of investigation.

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

Rational Drug Design Approach of Receptor Tyrosine Kinase Type III Inhibitors

2020 ◽  
Vol 26 (42) ◽  
pp. 7623-7640 ◽  
Author(s):  
Cheolhee Kim ◽  
Eunae Kim
Keyword(s):  
Drug Design ◽  
Tyrosine Kinase ◽  
Receptor Tyrosine Kinase ◽  
Synergistic Effects ◽  
Rational Drug Design ◽  
Computational Techniques ◽  
Biological Targets ◽  
Type Iii ◽  
Theoretical Approaches ◽  
Rational Drug

: Rational drug design is accomplished through the complementary use of structural biology and computational biology of biological macromolecules involved in disease pathology. Most of the known theoretical approaches for drug design are based on knowledge of the biological targets to which the drug binds. This approach can be used to design drug molecules that restore the balance of the signaling pathway by inhibiting or stimulating biological targets by molecular modeling procedures as well as by molecular dynamics simulations. Type III receptor tyrosine kinase affects most of the fundamental cellular processes including cell cycle, cell migration, cell metabolism, and survival, as well as cell proliferation and differentiation. Many inhibitors of successful rational drug design show that some computational techniques can be combined to achieve synergistic effects.

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