IANS

Artificial intelligence boosts drug delivery to eradicate malaria

Taipei, Nov 13 (IANS) Using the next generation Artificial Intelligence (AI)-based tools, a team of researchers has developed a new end-to-end drug discovery pipeline to eradicate malaria.

Insilico Taiwan, a Taipei-based subsidiary of Insilico Medicine, announced the results in a paper published in Scientific Reports, a journal published by the Nature Publishing Group.

Plasmodium falciparum, the most dangerous human malaria parasite, is believed to cause hundreds of millions of illnesses and about half a million deaths a year.

Plasmodium falciparum causes malaria in humans by destroying human haemoglobin through falcipain-2 (FP2).

“The control of malaria has been hindered by increasing resistance of malaria parasites to available drugs. New anti-malarial drugs, ideally directed against new targets, are urgently needed,” said the researchers.

To counter this challenge, the team from Insilico Taiwan extensively studied the mechanisms by which the protease inhibitor E64 approaches, interacts with, and inhibits FP2.

The results showed that the binding of E64 and FP2 are facilitated by the “amino acids of FP2 located within and nearby the previously identified binding pocket of FP2”.

This suggests that the anti-malarial drug design should not only focus on finding drug candidates that will bind tightly to the residues of established binding pocket, but also consider the need for the drug candidate to be able to bind to the residues surrounding the established binding pocket subsites.

“Insilico Taiwan is happy to present the work on malaria which potentially can help save millions of lives”, says Artur Kadurin, CEO, Insilico Medicine Taiwan.

“It is a fascinating experience for me working with our team on solving the malaria which remains one of the deadliest diseases killing about half a million people annually”, added Dr Emmanuel Salawu who holds a PhD in Bioinformatics.

–IANS
na/bg

(This story has not been edited by Newsd staff and is auto-generated from a syndicated feed.)
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