Assessment of Potential Phytochemicals Against Nipah Virus Using Computational Biology Approach

Nipah is a highly infectious and deadly virus with a mortality rate of 40 to 75%. As per the studies, the receptors on the surface of the host cell called ephrin-B3 (EFNB3) and ephrin-B2 (EFNB2), which are involved in cellular attachment, fusion, and virus entry, are recognized by means of G and F glycoproteins present on virus envelope. The computational design and analysis of the phytochemical library enabled by deep learning and analyzing the ligand-protein complex of NiV, is the focus of the study. The piece demonstrates how technology has completely changed the drug discovery process, making it quicker and improving its efficiency. The article covers the use of computer-aided drug discovery, molecular simulations, high-throughput screening (HTS), and the use of data analytics in drug development as well as technology-enabled instruments for drug analysis, including PyRx, Biovia development Studio, SwissADME, and GROMACS. The protein 3D structure (3D11) was downloaded by RCSB PDB Databank and refined through Biovia Discovery studio. The structure of 300 phytochemicals was downloaded in 3D SDF format and high throughput screening was performed using PyRx software. The results were further validated through SwissADME. Molecular Dynamics simulations were done on the selected phytochemical using GROMACS which utilized numerical integration techniques, such as the Verlet algorithm or the leapfrog algorithm. Because machine learning (ML) can analyze large and complex datasets, find patterns, and make predictions, it has become a key component of computational drug discovery.