Machine intelligence-guided selection of optimized inhibitor for human immunodeficiency virus (HIV) from natural products

Neeraj Kumar and Vishal Acharya

Abstract

The human immunodeficiency virus (HIV) connects to the cluster of differentiation (CD4) and any of the entry co-receptors (CCR5 and CXCR4); followed by unloading the viral genome, reverse transcriptase, and integrase enzymes within the host cell. The co-receptors facilitate the entry of virus and vital enzymes, leading to replication and pre-maturation of viral particles within the host. The protease enzyme transforms the immature viral vesicles into the mature virion. The pivotal role of co-receptors and enzymes in homeostasis and growth makes the crucial target for anti-HIV drug discovery, and the availability of X-ray crystal structures is an asset. Here, we used the machine intelligence-driven framework (A-HIOT) to identify and optimize target-based potential hit molecules for five significant protein targets from the ZINC15 database (natural products dataset). Following validation with dynamic motion behavior analysis and molecular dynamics simulation, the optimized hits were evaluated using in silico ADMET filtration. Furthermore, three molecules were screened, optimized, and validated: ZINC00005328058 for CCR5 and protease, ZINC000254014855 for CXCR4 and integrase, and ZINC000000538471 for reverse transcriptase. In clinical trials, the ZINC000254014855 and ZINC000254014855 were passed in primary screens for vif-HIV-1, and we reported the specific receptor as well as interactions. As a result, the validated molecules may be investigated further in experimental studies targeting specific receptors in order to design and synergize an anti-HIV regimen.

Computers in Biology and Medicine (IF 6.698)

Link: https://doi.org/10.1016/j.compbiomed.2022.106525