Derivation of the vibrational equations for polyphenyls from a molecular-fragment library

1984 ◽  
Vol 41 (1) ◽  
pp. 833-837
Author(s):  
A. T. Todorovskii

2012 ◽  
Vol 124 (17) ◽  
pp. 4247-4251 ◽  
Author(s):  
Rene Jørgensen ◽  
Lena Lisbeth Grimm ◽  
Nora Sindhuwinata ◽  
Thomas Peters ◽  
Monica M. Palcic


Author(s):  
Biancamaria Farina ◽  
Luciano Pirone ◽  
Gianluca D’Abrosca ◽  
Maria Della Valle ◽  
Luigi Russo ◽  
...  


2012 ◽  
Vol 51 (17) ◽  
pp. 4171-4175 ◽  
Author(s):  
Rene Jørgensen ◽  
Lena Lisbeth Grimm ◽  
Nora Sindhuwinata ◽  
Thomas Peters ◽  
Monica M. Palcic


1984 ◽  
Vol 5 (2) ◽  
pp. 170-174 ◽  
Author(s):  
Clifford E. Felder ◽  
Abraham Shanzer ◽  
Shneior Lifson


2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Karina van den Broek ◽  
Hubert Kuhn ◽  
Achim Zielesny


PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0123998 ◽  
Author(s):  
Saulo H. P. de Oliveira ◽  
Jiye Shi ◽  
Charlotte M. Deane


Biomolecules ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1518 ◽  
Author(s):  
Ana L. Chávez-Hernández ◽  
Norberto Sánchez-Cruz ◽  
José L. Medina-Franco

Natural products and semi-synthetic compounds continue to be a significant source of drug candidates for a broad range of diseases, including coronavirus disease 2019 (COVID-19), which is causing the current pandemic. Besides being attractive sources of bioactive compounds for further development or optimization, natural products are excellent substrates of unique substructures for fragment-based drug discovery. To this end, fragment libraries should be incorporated into automated drug design pipelines. However, public fragment libraries based on extensive collections of natural products are still limited. Herein, we report the generation and analysis of a fragment library of natural products derived from a database with more than 400,000 compounds. We also report fragment libraries of a large food chemical database and other compound datasets of interest in drug discovery, including compound libraries relevant for COVID-19 drug discovery. The fragment libraries were characterized in terms of content and diversity.



2013 ◽  
Vol 5 (S1) ◽  
Author(s):  
Andreas Truszkowski ◽  
Annamaria Fiethen ◽  
Hubert Kuhn ◽  
Achim Zielesny ◽  
Matthias Epple


Author(s):  
Francesca Ferrari ◽  
Maicol Bissaro ◽  
Simone Fabbian ◽  
Jessica de Almeida Roger ◽  
Stefano Mammi ◽  
...  

<p>In this manuscript, for the first time, we presented a fragment library and we validated its performance by comparison with a well-established technique for fragment screening as solution NMR. We were able to screen 400 different fragments producing a total of 1200 independent fragment-protein recognition pathways. As far as we know, this represents the largest screening based on Molecular dynamics ever reported. Our simulations successfully detected the true binders in the library in a prospective study, showing a notable agreement with a state-of-art screening we performed by NMR on the same dataset.</p>



2019 ◽  
Author(s):  
Naruki Yoshikawa ◽  
Geoffrey Hutchison

<div>Rapidly predicting an accurate three dimensional geometry of a molecule is a crucial task in cheminformatics and a range of molecular modeling. Fast, accurate, and open implementation of structure prediction is necessary for reproducible cheminformatics research. We introduce fragment-based coordinate generation for Open Babel, a widely accepted open source toolkit for cheminformatics. The new implementation significant improves speed and stereochemical accuracy, while retaining or improving accuracy of bond lengths, bond angles, and dihedral torsions. We first separate an input molecule into fragments by cutting at rotatable bonds. Coordinates of fragments are set according to the fragment library, which is prepared from open crystallographic databases. Since coordinates of multiple atoms are decided at once, coordinate prediction is accelerated over the previous rules-based implementation or the widely-used distance geometry methods in RDKit. This new implementation will be beneficial for a wide range of applications, including computational property prediction in polymers, molecular materials and drug design.</div>



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