Application of In silico Methods in the Design of Drugs for Neurodegenerative Diseases

2021 ◽  
Vol 21 ◽  
Author(s):  
Mohamad Haider ◽  
Anjali Chauhan ◽  
Sana Tariq ◽  
Dharam Pal Pathak ◽  
Nadeem Siddiqui ◽  
...  
Keyword(s):  
Drug Design ◽  
Neurodegenerative Diseases ◽  
In Silico ◽  
Brain Aging ◽  
Complex Nature ◽  
Computer Assisted ◽  
Complex Disorders ◽  
In Silico Admet ◽  
In Silico Studies ◽  
In Silico Methods

: Neurodegenerative diseases are complex disorders that cause neuron loss, brain aging and ultimately lead to death. These diseases are difficult to treat because of the complex nature of the nervous system and the available medicines are not able to heal them effectively. This fact implies the needs for novel therapeutics to be designed that are ready to stop or a minimum of retard the neurodegeneration process. These days, computer assisted drug design (CADD) approaches are a passage to extend the drug development efficiency and to reduce time and cost because traditional drug discovery is both time-consuming as well as costly. Computational or in silico methods came up with powerful tools in drug design against neurodegenerative diseases. This review presents the approaches and theoretical basis of CADD. Also, the successful applications of various in silico studies, including homology modeling, molecular docking, quantitative structure activity relationship (QSAR), molecular dynamic (MD), De Novo drug design, Pharmacophore-based drug design, Virtual Screening (VS), LIGPLOT Analysis, In silico ADMET and drug safety prediction, for treating neurodegenerative diseases have also been included in this review. Major emphasis is given to the Alzheimer’s disease and the Parkinson’s disease because these two are the most familiar neurodegenerative diseases.

scholarly journals Current Therapeutic Molecules and Targets in Neurodegenerative Diseases Based on in silico Drug Design

2018 ◽  
Vol 16 (6) ◽  
pp. 649-663 ◽  
Author(s):  
Sheikh Arslan Sehgal ◽  
Mirza A. Hammad ◽  
Rana Adnan Tahir ◽  
Hafiza Nisha Akram ◽  
Faheem Ahmad
Keyword(s):  
Drug Design ◽  
Neurodegenerative Diseases ◽  
In Silico ◽  
In Silico Drug Design ◽  
Therapeutic Molecules

In-silico Methods of Drug Design: Molecular Simulations and Free Energy Calculations

2019 ◽  
pp. 521-533 ◽  
Author(s):  
Fortunatus Chidolue Ezebuo ◽  
Prem P. Kushwaha ◽  
Atul K. Singh ◽  
Shashank Kumar ◽  
Pushpendra Singh
Keyword(s):  
Free Energy ◽  
Drug Design ◽  
In Silico ◽  
Molecular Simulations ◽  
Free Energy Calculations ◽  
Energy Calculations ◽  
In Silico Methods

Activity of Thiazine substituted 9-anilinoacridines against Corona virus (COVID19): An In-silico approach

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 482-490
Author(s):  
Kalirajan Rajagopal ◽  
Potlapati Varakumar ◽  
Baliwada Aparna ◽  
Vulsi Bodhya Sri ◽  
Gowramma Byran ◽  
...  
Keyword(s):  
In Silico ◽  
Binding Free Energy ◽  
Viral Disease ◽  
Docking Studies ◽  
Binding Modes ◽  
Main Protease ◽  
In Silico Admet ◽  
In Silico Studies ◽  
Life Threatening ◽  
Similar Mode

Coronavirus Disease 2019 (COVID-19), a life-threatening viral disease affected first in Wuhan, China, and quickly spread to more than 200 countries in the world in the year 2020. So many scientists are trying to discover novel drugs and vaccines for coronavirus and treatment for COVID-19. In the present article, in-silico studies have been performed to explore the binding modes of Thiazine substituted 9-anilinoacridines (1a-z) against SARS CoV 2 main protease (PDB id - 5R82) targeting the coronavirus using Schrodinger suit 2019-4. The molecular docking studies are performed by Glide module, in-silico ADMET screening was performed by Qik prop module, and the binding free energy of ligands was calculated using PRIME MM-GB/SA module of Schrodinger suite 2019-4, Maestro 21.2 version. From the in-silico results, Thiazine substituted 9-anilinoacridines like 1m, 1j, 1s and 1b are significantly active against SARS CoV 2 main protease with Glide score more than -5.4 when compared with the currently recommended drug for COVID19, Hydroxychloroquine (G score -5.47). The docking results of the Thiazine substituted 9-anilinoacridines exhibited similar mode of interactions with COVID19 and the residues GLN19, THR24, THR25, THR26, LEU27, HIE41, SER46, MET49, ASN142, GLN143, HIE164, MET165, ASP187, ARG188 and GLN189, play a crucial role in binding with ligands.

In Silico ADMET and Docking Studies of Thiazolidinedione-acetic-acid Hybrids as Antidiabetics with Cardioprotection

2020 ◽  
Vol 17 (12) ◽  
pp. 1475-1484
Author(s):  
Deepanwita Maji ◽  
Subir Samanta ◽  
Vaishali M. Patil
Keyword(s):  
Acetic Acid ◽  
Amino Acid ◽  
Side Effects ◽  
In Silico ◽  
Methyl Esters ◽  
Antioxidant Properties ◽  
Docking Studies ◽  
Single Amino Acid ◽  
In Silico Admet ◽  
In Silico Studies

Background: Type-2-diabetes mellitus is associated with many side effects affecting vital body organs, especially heart. Thiazolidinediones are potent antidiabetics. Studies have proven that amino-acids and peptides promote glucose transport, have antioxidant properties, and fewer side effects, thus we designed hybrids by combining amino-acid esters and peptide esters with 2, 4 thiazolidinedione acetic acid moiety which can act as antidiabetic agent with cardioprotection properties. Methodology: In vitro ADME, toxicity, and docking studies were performed using Qikprop3.1.OSIRIS, PROTOX (Prediction of Rodent Oral Toxicity), and FlexX 2.1.3, respectively. Results: All the designed molecules belong to three sub-series, i.e. 2, 4-dioxothiazolidine-5-acetic acid single amino acid hybrid methyl esters, 2, 4-dioxothiazolidine-5-acetic acid dipeptide hybrid methyl esters and 2, 4-dioxothiazolidine-5-acetic acid tripeptide hybrid methyl esters. All molecules were non-toxic. SSMA2, SSMA14, SSMA49, and SSDM50 showed good docking scores in 2PRG and 2UV4, respectively. Conclusion: The selected in silico studies helped to design hybrids with less toxicity, target specificity with dual activity as potential anti-diabetic and cardioprotective agents.

scholarly journals Activity of phytochemical constituents of Curcuma longa (turmeric) and Andrographis paniculata against coronavirus (COVID-19): an in silico approach

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Kalirajan Rajagopal ◽  
Potlapati Varakumar ◽  
Aparma Baliwada ◽  
Gowramma Byran
Keyword(s):  
Active Site ◽  
In Silico ◽  
Chemical Constituents ◽  
Curcuma Longa ◽  
Andrographis Paniculata ◽  
Significant Activity ◽  
Binding Modes ◽  
Main Protease ◽  
In Silico Admet ◽  
In Silico Studies

Abstract Background In early 2020, many scientists are rushing to discover novel drugs and vaccines against the coronavirus, and treatments for COVID-19, because coronavirus disease 2019 (COVID-19), a life-threatening viral disease, affected first in China and quickly spread throughout the world. In this article, in silico studies have been performed to explore the binding modes of chemical constituents for natural remedies like Curcuma longa (turmeric) and Andrographis paniculata against COVID-19 (PDB ID 5R82) targeting coronavirus using Schrodinger suit 2019-4. The molecular docking studies are performed by the Glide module, in silico ADMET screening was performed by the QikProp module, and binding energy of ligands was calculated using the Prime MM-GB/SA module. Results The chemical constituents from turmeric like cyclocurcumin and curcumin and from Andrographis paniculata like andrographolide and dihydroxy dimethoxy flavone are significantly binding with the active site of SARS CoV-2 main protease with Glide score more than − 6 when compared to the currently used drugs hydroxychloroquine (− 5.47) and nelfinavir (− 5.93). When compared to remdesivir (− 6.38), cyclocurcumin from turmeric is significantly more active. The docking results of the compounds exhibited similar mode of interactions with SARS CoV-2. Main protease and the residues THR24, THR25, THR26, LEU27, SER46, MET49, HIE41, GLN189, ARG188, ASP187, MET165, HIE164, PHE181, and THR54 play a crucial role in binding with ligands. Conclusion Based on in silico investigations, the chemical constituents from turmeric like cyclocurcumin and curcumin and from Andrographis paniculata like andrographolide and dihydroxy dimethoxy flavone, significantly binding with the active site of SARS CoV-2 main protease, may produce significant activity and be useful for further development.

Computer-Aided Anticancer Drug Design: In vitro and In silico Studies of New Iminocoumarin Derivative

2021 ◽  
pp. 130539
Author(s):  
Serda Kecel-Gunduz ◽  
Yasemin Budama-Kilinc ◽  
Bahar GOK ◽  
Bilge Bicak ◽  
Gizem Akman ◽  
...  
Keyword(s):  
Drug Design ◽  
Anticancer Drug ◽  
In Silico ◽  
In Silico Studies ◽  
Computer Aided

An overview of computer-aided drug design tools and recent applications in designing of antidiabetic agents

2020 ◽  
Vol 21 ◽  
Author(s):  
Paranjeet Kaur ◽  
Gopal Khatik
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Structure Prediction ◽  
Material Design ◽  
Computer Assisted ◽  
Antidiabetic Agents ◽  
Computer Aided Drug Design ◽  
In Silico Studies ◽  
Computer Aided

Background: In this fast-growing era, high throughput data is now being so easily accessed by getting transformed into datasets which store the information. Such information is valuable to optimize the hypothesis and drug design via computer-aided drug design (CADD). Nowadays, we can explore the role of CADD in various disciplines like Nanotechnology, Biochemistry, Medical Sciences, Molecular Biology, etc. Methods: We searched the valuable literature using a pertinent database with given keywords like computer-aided drug design, antidiabetic, drug design, etc. We retrieved all valuable articles which are recent and discussing the role of computation in the designing of antidiabetic agents. Results: To facilitate the drug discovery process, the computational approach has set landmarks in the whole pipeline for drug discovery from target identification and mechanism of action to the identification of leads and drug candidates. Along with this, there is a determined endeavor to describe the significance of in-silico studies in predicting the absorption, distribution, metabolism, excretion, and toxicity profile. Thus, globally CADD is accepted with a variety of tools for studying QSAR, virtual screening, protein structure prediction, quantum chemistry, material design, physical and biological property prediction. Conclusion: Computer-assisted tools are used as the drug discovery tool in the area of different diseases, and here we reviewed the collaborative aspects of information technologies and chemoinformatics tools in the discovery of antidiabetic agents keeping in-view of the growing importance for treating diabetes.

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