Redefining a new frontier in alkaptonuria therapy with AI-driven drug candidate design via in- silico innovation.

Abstract

A rare metabolic condition called alkaptonuria (AKU) is caused by a decrease in homogentisate 1,2 dioxygenase (HGO) activity due to a mutation in homogentisate dioxygenase (HGD) gene. Homogentisic acid is a byproduct of the catabolism of tyrosine and phenylalanine that darkens the urine and accumulates in connective tissues which causes an agonizing arthritis. Employing the use of deep learning artificial intelligence (AI) drug design, this study aims to alleviate the current toxicity of the AKU drugs currently inuse, particularly nitisinone, by utilizing the natural flavanolkaempferol molecule as a 4-hydroxyphenylpyruvate dioxygenase inhibitor. Kaempferol was employed to generate three effective de novo drug candidates targeting the enzyme4-hydroxyphenylpyruvate dioxygenase using an AI drug design tool. We present novel AIK formulations in the present study. The AIK's (Artificial Intelligence Kaempferol) examination of drug-likeliness among the three led to its choice as a possible target. The toxicity assessment research ofAIK demonstrates that it is not only safer to use than othertreatments, but also more efficient. The docking of theAIGT with 4-hydroxyphenylpyruvate dioxygenase, whichrevealed a binding affinity of around-9.099 kcal/mol, highlights the AIK's potential as a therapeutic candidate. Aninnovative approach to deal with challenging circumstances is thus presented in this study by new formulations kaempferol that have been meticulously designed by AI. The results of the invitro tests must be confirmed invivo, even though AI-designed AIK is effective and sufficiently safe as computed.