Fleming FF, Yao L, Ravikumar PC, Funk L, Shook BC. Nitrile-Containing Pharmaceuticals: Efficacious Roles of the Nitrile Pharmacophore. Journal of Medicinal Chemistry. 2010:100830145932067.
Available at: http://pubs.acs.org/doi/abs/10.1021/jm100762r
This paper is nice review pointing out nitrile (CN) group in medicinal chemistry.
1) carbonyl isoster
2) hydroxyl and carboxyl isoster
3) strong electronegativity
4) azomethine-water isoster
5) protease transition state mimics (DPP-4 or Cathepsin K)
6) halogen isoster
7) tool of improving ADMETox by reducing lipophilicity and metabolic vulnerability
Especially, azomethin-water isosterism is quite interesting because this suggests that replacement of pyridine with cyanoaryl is reasonable drug design if pyridine forms hydrogen bonding with solvent water.
Zhou C, Garcia-Calvo M, Pinto S, et al. Design and Synthesis of Prolylcarboxypeptidase (PrCP) Inhibitors To Validate PrCP As A Potential Target for Obesity. Journal of Medicinal Chemistry. 2010:100921101803027.
Available at: http://pubs.acs.org/doi/abs/10.1021/jm101013m.
Because of relatively low hit rate of serine protease inhibitors, medicinal chemist needs to design a inhibitor by a strategic approach. Previously, there are neumerous reports for DPP-4 or Factor Xa inhibitor as serine protease inhibitor with various approach, and the essence of the design is reproted separately in various papers. On the other hand, this report involves a design of introduction, development, turn, and conclusion, so that this context is regarded as a good example of rational design of serine protease inhibitor.
The key flow is below.
Notisification of ketone as a transition-state mimicker→ breakdown of endogeneous peptides→ focus library→ switch of covalent ketone with non-covalent azole→ S2 pokect investigation→ S1 site optimization→ improving property (notewarthy that the AIB unit used in Ghrelin agonist program is introduced)→ serendipitous discovery of beta-methyl impact→ POC of animal model with knockout mice
Lajiness JP, Robertson WM, Dunwiddie I, et al. Design, Synthesis, and Evaluation of Duocarmycin O-Amino Phenol Prodrugs Subject to Tunable Reductive Activation. Journal of medicinal chemistry. 2010. Available at: http://www.ncbi.nlm.nih.gov/pubmed/20942408.
Anti-tumor agent CC-1065 and Duocarmycin has cyclopropylindoline as a common substructure. In this report, prodrug strategy acchieved that active compound is only released in tumor enviroment. Prodrug compounds are shown in Fig. 3. Those compounds have N-O linker as the prodrug linkage. In hypoxic tumor environment, N-O bond is cleaved, subsequent phenol is reacted with chloromethylene remotely to form active cyclopropylindoline.
Yang Y, Chen H, Nilsson I, Muresan S, Engkvist O. Investigation of the Relationship between Topology and Selectivity for Druglike Molecules. Journal of Medicinal Chemistry. 2010:101013093941041. Available at: http://pubs.acs.org/doi/abs/10.1021/jm1008456.
This pepar analyzes relationship of molecular topology and promiscuity. Topology has quite nice correlation with promiscuity, and It is noteworthy that this correlation is indepenent from lipophilicity. Considering this result, we can find selective compounds which are outlayers for clogP>3 violation!!!
Q: Why would the high f(MF) scaffolds show high promiscuity?
I would aruge that the binding mode can be a major difference here. The high f(MF) scaffolds show specific binding modes while the low valued not.
Only specific binding mode can ensure a ligand to show significant affinities at the binding sites. If a ligand has more than one binding modes, presumably its binding affinity would not be shown very high and thus its promiscuity would not be detected.
-Just a thought
PS: The previledged structures can be a good example of the high f(MF) scaffolds. The previledged structures are special scaffolds found with some different ligand types . And they are usually rigid multi-ring systems, thus with the high f(MF). Many of them show high affinities towards their targets.
Thank you for your thoughtful comments. I think f(MF) is good indicator for explanation of various aspects such as your points. I wonder if AstraZeneca's chemists use f(MF) on drug design & optimization.