Efficiently Predicting HIV-1 Protease Cleavage Sites by Using Deep CNN-Assisted Hybridised Approach

Acquired Immuno Deficiency Syndrome (AIDS) is considered a vitaldanger over the feasible development and on account of its pestilenceeffect and nonattendance of reparable medicines. HIV-1 AIDS can beconstrained by utilising protease inhibitors. Different procedures forpredicting sites are utilised to comprehend the highlighted sites of thoseproteases. Arrangement-based sites like physiochemical elements andconstruction-based sites are separated from HIV-1 proteases. In thisarticle, a procedure for choosing those sites using a deep CNN-assistedhybridised approach will be used for effectively predicting cleavage sites.The proposed methodology was evaluated based on various Type-1 andType-2 parameters. The proposed approach gives superior results onType-1 and Type-2 parameters. Data746_setset provides an accuracy of0.924, data 1625_set provides an accuracy of 0.946, Data_schilling_setprovides an accuracy of 0.9389, Data_impens_set provides an accuracy of 0.911 and average dataset provides an accuracy of 0.921 for Type-1 parameters. Data746_setset provides an accuracy of 0.84664, data 1625_set provides an accuracy of 0.34179, data_schilling_set providesan accuracy of 0.69529 and data_impens_set provides an accuracy of0.59511 for Type-2 parameters. Saini S, Kaur G. Efficiently Predicting HIV-1 Protease Cleavage Sites by Using Deep CNN-Assisted Hybridised Approach. J Commun Dis. 2025;57(1):91-95. DOI: https://doi.org/10.24321/0019.5138.202511