scholarly journals Delivery of pharmaceutics to bone: nanotechnologies, high-throughput processing and in silico mathematical models

2016 ◽  
Vol 31 ◽  
pp. 355-381 ◽  
Author(s):  
IS Alencastre ◽  
◽  
DM Sousa ◽  
CJ Alves ◽  
L Leitão ◽  
...  
Keyword(s):  
Mathematical Models ◽  
High Throughput ◽  
In Silico

A novel compound active against SARS-CoV-2 targeting Uridylate-specific endoribonuclease (NendoU/NSP15): In silico and in vitro investigations

2021 ◽  
Author(s):  
Sumit Kumar ◽  
Yash Gupta ◽  
Samantha Zak ◽  
Charu Upadhyay ◽  
Neha Sharma ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
In Silico ◽  
Specific Enzyme ◽  
Cyclic Phosphates ◽  
High Throughput Virtual Screening

NendoU (NSP15) is an Mn(2+)-dependent, uridylate-specific enzyme, which leaves 2'-3'-cyclic phosphates 5' to the cleaved bond. Our in-house library was subjected to high throughput virtual screening (HTVS) to identify compounds...

Discovery of indenopyrazoles as EGFR and VEGFR-2 tyrosine kinase inhibitors by in silico high-throughput screening

2008 ◽  
Vol 18 (1) ◽  
pp. 285-288 ◽  
Author(s):  
Taikou Usui ◽  
Hyun Seung Ban ◽  
Junpei Kawada ◽  
Takatsugu Hirokawa ◽  
Hiroyuki Nakamura
Keyword(s):  
Tyrosine Kinase ◽  
Tyrosine Kinase Inhibitors ◽  
High Throughput ◽  
In Silico ◽  
High Throughput Screening ◽  
Kinase Inhibitors

scholarly journals High Throughput in Silico Identification of Novel Phytochemical Inhibitors for the Master Regulator of Inflammation (TNFα)

2021 ◽  
Author(s):  
Pratap Kumar Parida ◽  
Dipak Paul ◽  
Debamitra Chakravorty
Keyword(s):  
Small Molecule ◽  
High Throughput ◽  
In Silico ◽  
Small Molecule Inhibitors ◽  
Binding Free Energy ◽  
Natural Compounds ◽  
Free Energy Calculations ◽  
Compound Libraries ◽  
Relative Binding

<p><a>The over expression of Tumor necrosis factor-α (TNFα) has been implicated in a variety of disease and is classified as a therapeutic target for inflammatory diseases (Crohn disease, psoriasis, psoriatic arthritis, rheumatoid arthritis).Commercially available therapeutics are biologics which are associated with several risks and limitations. Small molecule inhibitors and natural compounds (saponins) were identified by researchers as lead molecules against TNFα, however, </a>they were often associated with high IC50 values which can lead to their failure in clinical trials. This warrants research related to identification of better small molecule inhibitors by screening of large compound libraries. Recent developments have demonstrated power of natural compounds as safe therapeutics, hence, in this work, we have identified TNFα phytochemical inhibitors using high throughput <i>in silico </i>screening approaches of 6000 phytochemicals followed by 200 ns molecular dynamics simulations and relative binding free energy calculations. The work yielded potent hits that bind to TNFα at its dimer interface. The mechanism targeted was inhibition of oligomerization of TNFα upon phytochemical binding to restrict its interaction with TNF-R1 receptor. MD simulation analysis resulted in identification of two phytochemicals that showed stable protein-ligand conformations over time. The two compounds were triterpenoids: Momordicilin and Nimbolin A with relative binding energy- calculated by MM/PBSA to be -190.5 kJ/Mol and -188.03 kJ/Mol respectively. Therefore, through this work it is being suggested that these phytochemicals can be used for further <i>in vitro</i> analysis to confirm their inhibitory action against TNFα or can be used as scaffolds to arrive at better drug candidates.</p>

scholarly journals Providing the ‘Best’ Lipophilicity Assessment in a Drug Discovery Environment

2021 ◽  
Author(s):  
george chang ◽  
Nathaniel Woody ◽  
Christopher Keefer
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
High Throughput ◽  
In Silico ◽  
Shake Flask ◽  
Chromatographic Method ◽  
Learning Approach ◽  
Rule Based ◽  
Machine Learning Approach ◽  
High Throughput Screens

Lipophilicity is a fundamental structural property that influences almost every aspect of drug discovery. Within Pfizer, we have two complementary high-throughput screens for measuring lipophilicity as a distribution coefficient (LogD) – a miniaturized shake-flask method (SFLogD) and a chromatographic method (ELogD). The results from these two assays are not the same (see Figure 1), with each assay being applicable or more reliable in particular chemical spaces. In addition to LogD assays, the ability to predict the LogD value for virtual compounds is equally vital. Here we present an in-silico LogD model, applicable to all chemical spaces, based on the integration of the LogD data from both assays. We developed two approaches towards a single LogD model – a Rule-based and a Machine Learning approach. Ultimately, the Machine Learning LogD model was found to be superior to both internally developed and commercial LogD models.<br>

In Silico Analysis of SNPs and Other High-Throughput Data

2007 ◽  
pp. 267-286
Author(s):  
Neema Jamshidi ◽  
Thuy D. Vo ◽  
Bernhard O. Palsson
Keyword(s):  
High Throughput ◽  
In Silico ◽  
High Throughput Data

scholarly journals ANALYSIS OF MOLECULAR DOCKING EFFICIENCY OF CLEISTANTHIN-A, AS AN ALTERNATIVE FOR NICOTINE ADDICTION

2018 ◽  
Vol 10 (4) ◽  
pp. 98
Author(s):  
A. Amala Lourthuraj ◽  
M. Masilamani Selvam ◽  
Bharathi Ravikrishnan ◽  
M. Vinoth ◽  
Waheeta Hopper
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Protein Data Bank ◽  
Acetylcholine Receptor ◽  
Nicotinic Acetylcholine Receptor ◽  
High Throughput ◽  
In Silico ◽  
Data Bank ◽  
Tobacco Consumption ◽  
Nicotinic Acetylcholine

Objective: The present research was aimed to understand the molecular docking efficiency of a plant-derived compound cleistanthin-A and a common ingredient in tobacco consumption nicotine with nicotinic acetylcholine receptor (nAChR).Methods: The 3-D structure of nAChR was retrieved from the protein data bank (ID 5AFH). Ligand was obtained from the PUBCHEM. The in silico protocol comprised of three steps: high-throughput virtual screening (HTVS), standard preci­sion (SP) and extra precision (XP). The screened molecules were ranked accordingly using glide score. Schrödinger tool was used to perform the docking analysis.Results: The binding efficiency of the nicotine and cleistanthin-A was found to be docked at the cys-cys loop of the receptor. Based upon the glide score and glide energy it can be reported that, nicotine binding can be inhibited by the binding of cleistanthin-A to the nAChR.Conclusion: The docking efficiency of cleistanthin-A was good compared to nicotine towards nAChR. Hence, cleistanthin–A was derived as a better choice as an alternative for nicotine in smoke therapy.

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