scholarly journals In silico Design, Virtual Screening and Synthesis of Novel Electrolytic Solvents

2019 ◽  
Vol 38 (10) ◽  
pp. 1900014 ◽  
Author(s):  
G. Marcou ◽  
B. Flamme ◽  
G. Beck ◽  
A. Chagnes ◽  
O. Mokshyna ◽  
...  
ChemInform ◽  
2005 ◽  
Vol 36 (39) ◽  
Author(s):  
Yovani Marrero-Ponce ◽  
Maite Iyarreta-Veitia ◽  
Alina Montero-Torres ◽  
Carlos Romero-Zaldivar ◽  
Carlos A. Brandt ◽  
...  

2005 ◽  
Vol 45 (4) ◽  
pp. 1082-1100 ◽  
Author(s):  
Yovani Marrero-Ponce ◽  
Maité Iyarreta-Veitía ◽  
Alina Montero-Torres ◽  
Carlos Romero-Zaldivar ◽  
Carlos A. Brandt ◽  
...  

2019 ◽  
Author(s):  
Filip Fratev ◽  
Denisse A. Gutierrez ◽  
Renato J. Aguilera ◽  
suman sirimulla

AKT1 is emerging as a useful target for treating cancer. Herein, we discovered a new set of ligands that inhibit the AKT1, as shown by in vitro binding and cell line studies, using a newly designed virtual screening protocol that combines structure-based pharmacophore and docking screens. Taking together with the biological data, the combination of structure based pharamcophore and docking methods demonstrated reasonable success rate in identifying new inhibitors (60-70%) proving the success of aforementioned approach. A detail analysis of the ligand-protein interactions was performed explaining observed activities.<br>


2013 ◽  
Vol 999 (999) ◽  
pp. 1-15
Author(s):  
H.K. Ho ◽  
G. Nemeth ◽  
Y.R. Ng ◽  
E. Pang ◽  
C. Szantai-Kis ◽  
...  

2020 ◽  
Vol 27 (38) ◽  
pp. 6523-6535 ◽  
Author(s):  
Antreas Afantitis ◽  
Andreas Tsoumanis ◽  
Georgia Melagraki

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.


Sign in / Sign up

Export Citation Format

Share Document