Naratriptan is as effective as sumatriptan for the treatment of migraine attacks when used properly. A mini-review

Migraine and Its Treatment from the Medicinal Chemistry Perspective

Migraine is a disease of neurovascular origin that affects the quality of life of more than one billion people and ranks sixth among the most common diseases in the world. Migraine is characterized by a moderate or severe recurrent and throbbing headache, accompanied by nausea, vomiting, and photo-phonophobia. It usually starts in adolescence and is twice as common in women as in men. It is classified as with or without aura and has chronic or acute treatment types according to the frequency of occurrence. In acute treatment, analgesics that relieve pain in the fastest way are preferred, while there are different options in chronic treatment. While non-specific methods were used in the treatment of migraine until the 1950s, triptans, ditans, and CGRP-receptor-dependent therapies (monoclonal antibodies and gepants) started to be used in the clinic more recently. In this Review, we focus on the synthesis, side effects, and pharmacological and pharmacokinetic properties of FDA-approved drugs used in acute and preventive-specific treatment of migraine.

DMSO/SOCl2-Enabled Synthesis of 3-Chloroindoles via Desulfonylative Chlorocyclization of N,N-Disubstituted 2-Alkynylanilines

The application of the DMSO/SOCl2 system enabled the intramolecular cyclization/chlorination of N,N-disubstituted 2-alkynylanilines, leading to the synthesis of a series of 3-chloroindoles with moderate to good yields. Differing from the previously reported interrupted Pummerer reaction featuring the introduction of SMe moiety, the current approach adopted an alternative pathway that realized the incorporation of chlorine atom to the indole skeleton via a desulfonylative chlorocyclization process.

A novel method for drug-target interaction prediction based on graph transformers model

BACKGROUND

Drug-target interactions (DTIs) prediction becomes more and more important for accelerating drug research and drug repositioning. Drug-target interaction network is a typical model for DTIs prediction. As many different types of relationships exist between drug and target, drug-target interaction network can be used for modeling drug-target interaction relationship. Recent works on drug-target interaction network are mostly concentrate on drug node or target node and neglecting the relationships between drug-target.

RESULTS

We propose a novel prediction method for modeling the relationship between drug and target independently. Firstly, we use different level relationships of drugs and targets to construct feature of drug-target interaction. Then, we use line graph to model drug-target interaction. After that, we introduce graph transformer network to predict drug-target interaction.

CONCLUSIONS

This method introduces a line graph to model the relationship between drug and target. After transforming drug-target interactions from links to nodes, a graph transformer network is used to accomplish the task of predicting drug-target interactions.