Background:
As a matter of fact, nitrogen as a hetero atom among other atoms has had an
important role in active biological compounds. Since heterocyclic molecules with nitrogen are highly
demanded due to biological properties, 4-phenylurazole as a compound containing nitrogen might be
important in the multicomponent reaction used in agrochemicals, and pharmaceuticals. Considering the case
of fused derivatives “pyrazolourazoles” which are highly applicable because of their application for analgesic,
antibacterial, anti-inflammatory and antidiabetic activities as HSP-72 induction inhibitors (I and III) and novel
microtubule assembly inhibitors. It should be mentioned that spiro-pyrazole also has biological activities like
cytotoxic, antimicrobial, anticonvulsant, antifungal, anticancer, anti-inflammatory, and cardiotonic activities.
Objective:
Urazole has been used in many heterocyclic compounds which are valuable in organic syntheses.
This review disclosed the advances in the use of urazole as the starting material in the synthesis of various
biologically active molecules from 2006 to 2019.
Conclusion:
Compounds of urazole (1,2,4-triazolidine-3,5-dione) are the most important molecules which are
highly active from the biological perspective in the pharmaceuticals as well as polymers. In summary, many
protocols for preparations of the urazole derivatives from various substrates in multi-component reactions
have been reported from different aromatic and aliphatic groups which have had carbonyl groups in their
structures. It is noted that several catalysts have been synthesized to afford applicable molecules with urazole
scaffolds. In some papers, being environmentally friendly, short time reactions and high yields are highlighted
in the protocols. There is a room to synthesize new catalysts and perform new reactions by manipulating
urazole to produce biologically active compounds, even producing chiral urazole component as many groups
of chiral urazole compounds are important from biological perspective.
Multi-assignment clustering: Machine learning from a biological perspective