Faculty Opinions recommendation of The impact of aromatic ring count on compound developability--are too many aromatic rings a liability in drug design?

Author(s):  
John Proudfoot
CrystEngComm ◽  
2019 ◽  
Vol 21 (42) ◽  
pp. 6432-6445 ◽  
Author(s):  
Ali Samie ◽  
Alireza Salimi ◽  
Jered C. Garrison

In C–H⋯π interaction, the relative π-electron localization in aromatic ring led to the change of contact position from centre to edges of the ring (C–H⋯πe) which was confirmed by experimental evidences, computational criteria, and database analysis.


Molecules ◽  
2019 ◽  
Vol 24 (3) ◽  
pp. 595
Author(s):  
Laura Salacz ◽  
Nicolas Girard ◽  
Jean Suffert ◽  
Gaëlle Blond

Cycloadditions have emerged as some of the most useful reactions for the formation of polycyclic compounds. The carbonylative cycloaddition of triynes can lead to carbonylative and non-carbonylative competitive pathways, each leading to the formation of an aromatic ring. We report herein the one-pot synthesis of fully- and unsymmetrically-substituted tetracyclic 6,5,7,5-troponic and 6,5,6,5-benzenoid scaffolds using pre-organized triynes showing the competition between these two pathways.


Author(s):  
Oleg Y. Borbulevych ◽  
Roger I. Martin ◽  
Lance M. Westerhoff

Abstract Conventional protein:ligand crystallographic refinement uses stereochemistry restraints coupled with a rudimentary energy functional to ensure the correct geometry of the model of the macromolecule—along with any bound ligand(s)—within the context of the experimental, X-ray density. These methods generally lack explicit terms for electrostatics, polarization, dispersion, hydrogen bonds, and other key interactions, and instead they use pre-determined parameters (e.g. bond lengths, angles, and torsions) to drive structural refinement. In order to address this deficiency and obtain a more complete and ultimately more accurate structure, we have developed an automated approach for macromolecular refinement based on a two layer, QM/MM (ONIOM) scheme as implemented within our DivCon Discovery Suite and "plugged in" to two mainstream crystallographic packages: PHENIX and BUSTER. This implementation is able to use one or more region layer(s), which is(are) characterized using linear-scaling, semi-empirical quantum mechanics, followed by a system layer which includes the balance of the model and which is described using a molecular mechanics functional. In this work, we applied our Phenix/DivCon refinement method—coupled with our XModeScore method for experimental tautomer/protomer state determination—to the characterization of structure sets relevant to structure-based drug design (SBDD). We then use these newly refined structures to show the impact of QM/MM X-ray refined structure on our understanding of function by exploring the influence of these improved structures on protein:ligand binding affinity prediction (and we likewise show how we use post-refinement scoring outliers to inform subsequent X-ray crystallographic efforts). Through this endeavor, we demonstrate a computational chemistry ↔ structural biology (X-ray crystallography) "feedback loop" which has utility in industrial and academic pharmaceutical research as well as other allied fields.


2018 ◽  
Vol 1569 ◽  
pp. 101-109 ◽  
Author(s):  
Julie Robinson ◽  
Mark A. Snyder ◽  
Chris Belisle ◽  
Jia-li Liao ◽  
Hong Chen ◽  
...  

1996 ◽  
Vol 1 (11) ◽  
pp. 474-480 ◽  
Author(s):  
Lee J. Beeley ◽  
D. Malcolm Duckworth
Keyword(s):  

2014 ◽  
Vol 13 (2) ◽  
pp. 87-108 ◽  
Author(s):  
Pierfausto Seneci ◽  
Giorgio Fassina ◽  
Vladimir Frecer ◽  
Stanislav Miertus

Abstract The review will focus on the aspects of combinatorial chemistry and technologies that are more relevant in the modern pharmaceutical process. An historical, critical introduction is followed by three chapters, dealing with the use of combinatorial chemistry/high throughput synthesis in medicinal chemistry; the rational design of combinatorial libraries using computer-assisted combinatorial drug design; and the use of combinatorial technologies in biotechnology. The impact of “combinatorial thinking” in drug discovery in general, and in the examples reported in details, is critically discussed. Finally, an expert opinion on current and future trends in combinatorial chemistry and combinatorial technologies is provided.


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