Employing Machine Learning-Based QSAR for Targeting Zika Virus NS3 Protease: Molecular Insights and Inhibitor Discovery.

Zika virus infection is a mosquito-borne viral disease that has become a global health concern recently. Zika virus belongs to the Flavivirus genus and is primarily transmitted by Aedes mosquitoes. Prevention of Zika virus infection involves avoiding mosquito bites by using repellent, wearing protective clothing, and staying in screened areas, especially for pregnant women. Treatment focuses on managing symptoms with rest, fluids, and acetaminophen, with close monitoring for pregnant women. Currently, there is no specific antiviral treatment or vaccine for the Zika virus, highlighting the importance of prevention strategies to control its spread. Therefore, in this study, the Zika virus non-structural protein NS3 was targeted to inhibit Zika infection by identifying the novel inhibitor through an in silico approach. Here, 2864 natural compounds were screened using a machine learning-based QSAR model, and later docking was performed to select the potential target. Subsequently, Tanimoto similarity and clustering were performed to obtain the potential target. The three most potential compounds were obtained: (a) 5297, (b) 432449, and (c) 85137543. The protein-ligand complex's stability and flexibility were then investigated by dynamic modelling. The 300 ns simulation showed that 5297 exhibited the steadiest deviation and constant creation of hydrogen bonds. Compared to the other compounds, 5297 demonstrated a superior binding free energy (ΔG = -20.81 kcal/mol) with the protein when the MM/GBSA technique was used. The study determined that 5297 showed significant therapeutic potential and justifies further experimental investigation as a possible inhibitor of the NS2B-NS3 protease target implicated in Zika virus infection.