scholarly journals In Silico Modelling of Transdermal and Systemic Kinetics of Topically Applied Solutes: Model Development and Initial Validation for Transdermal Nicotine

2016 ◽  
Vol 33 (7) ◽  
pp. 1602-1614 ◽  
Author(s):  
Tao Chen ◽  
Guoping Lian ◽  
Panayiotis Kattou
Keyword(s):  
In Silico ◽  
Model Development ◽  
Initial Validation ◽  
Transdermal Nicotine ◽  
In Silico Modelling ◽  
Kinetics Of

Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME

2020 ◽  
Vol 27 (38) ◽  
pp. 6523-6535 ◽  
Author(s):  
Antreas Afantitis ◽  
Andreas Tsoumanis ◽  
Georgia Melagraki
Keyword(s):  
Data Analysis ◽  
Virtual Screening ◽  
In Silico ◽  
Model Development ◽  
In Silico Analysis ◽  
Material Design ◽  
Efficient Solutions ◽  
Biological Data ◽  
Cost Efficient ◽  
Silico Analysis

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

scholarly journals In-silico modelling of fullerene and fullerene adsorbed by nO2 molecules (n(O2)@Cm with n = 1, 2, 4 and m = 48 and 60) as potential SARS-CoV-2 inhibitors

2021 ◽  
Vol 44 (3) ◽  
Author(s):  
Meziane Brahimi ◽  
Djamila SELLAM ◽  
Afaf Bouchoucha ◽  
Yassamina Arbia ◽  
Hadjer Merazka ◽  
...  
Keyword(s):  
In Silico ◽  
In Silico Modelling

In Silico Modelling of Acceptor Materials by End-capped and π-linker modifications for High-Performance Organic Solar Cells: Estimated PCE > 18%

2021 ◽  
pp. 113555
Author(s):  
Muhammad Yasir Mehboob ◽  
Rania Zaier ◽  
Riaz Hussain ◽  
Muhammad Adnan ◽  
Malik Muhammad Asif Iqbal ◽  
...  
Keyword(s):  
Solar Cells ◽  
Organic Solar Cells ◽  
In Silico ◽  
High Performance ◽  
In Silico Modelling

scholarly journals EP-1998: In silico modelling of the impact of the fractionation for hypofractionated prostate treatments

2018 ◽  
Vol 127 ◽  
pp. S1086-S1087
Author(s):  
G. Delpon ◽  
J. N'Guessan ◽  
P. Paul-Gilloteaux ◽  
K. Clément-Colmou ◽  
V. Potiron ◽  
...  
Keyword(s):  
In Silico ◽  
In Silico Modelling ◽  
The Impact

scholarly journals In silico modelling of hormone response elements

2006 ◽  
Vol 7 (Suppl 4) ◽  
pp. S27 ◽  
Author(s):  
Maria Stepanova ◽  
Feng Lin ◽  
Valerie Lin
Keyword(s):  
In Silico ◽  
Hormone Response ◽  
Response Elements ◽  
Hormone Response Elements ◽  
In Silico Modelling

scholarly journals Psychomotor stimulant effects of α-pyrrolidinovalerophenone (αPVP) enantiomers correlate with drug binding kinetics at the dopamine transporter

2021 ◽  
Author(s):  
Marco Niello ◽  
Spyridon Sideromenos ◽  
Ralph Gradisch ◽  
Ronan O'Shea ◽  
Jakob Schwazer ◽  
...  
Keyword(s):  
Dopamine Transporter ◽  
In Silico ◽  
Drug Binding ◽  
Binding Kinetics ◽  
Irreversible Binding ◽  
Fast Kinetics ◽  
Slow Dissociation ◽  
Kinetics Of

Abstract α-Pyrrolidinovalerophenone (αPVP) is a psychostimulant and drug of abuse associated with severe intoxications in humans. αPVP exerts long-lasting psychostimulant effects, when compared to the classical dopamine transporter (DAT) inhibitor cocaine. Here, we compared the two enantiomeric forms of αPVP, the R- and the S-αPVP, with cocaine using a combination of in silico, in vitro and in vivo approaches. We found that αPVP enantiomers substantially differ from cocaine in their binding kinetics. The two enantiomers differ from each other in their association rates. However, they show similar slow dissociation rates leading to pseudo-irreversible binding kinetics at DAT. The pseudo-irreversible binding kinetics of αPVP is responsible for the observed non-competitive pharmacology and it correlates with persistent psychostimulant effects in mice. Thus, the slow binding kinetics of αPVP enantiomers profoundly differ from the fast kinetics of cocaine both in vitro and in vivo, suggesting drug-binding kinetics as a potential driver of psychostimulant effects in vivo.

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