Identification and selection of potential TNF-α inhibitors as anti-encephalitis candidates by using in silico assisted drug design

Encephalitis, an inflammatory disorder of the brain caused by infections or autoimmune responses, is still a major global health concern due to its high morbidity, mortality, and long-term neurological consequences. The available therapy alternatives are usually confined, and conventional drug discovery procedures are both resource-intensive and time-consuming. These challenges underscore the critical need for novel tools to speed up therapy development, which could revolutionize treatment regimens and enhance results for impacted people globally. To identify, optimize & selection of TNF-α inhibitors as anti-encephalitis candidates by using in silico assisted drug design approach. Computer-aided drug design (CADD) is a revolutionize tool for identifying potential anti-encephalitis candidates through its ability to simulate molecular interactions, predict drug-target affinities, and optimize pharmacokinetic properties with molecular dynamics simulation. In this study, we have employed comprehensive in silico method to find effective therapeutic compounds that targets the TNF-α receptor in encephalitis. An in silico screening of nearly 600 isatin derivatives were conducted by using data from the PubChem database. It was carried out using the Lipinski rule of five in addition to other criteria. After filtering, 48 Isatin derivatives were selected, and six compounds with binding affinities exceeding −8.0 kcal/mol were identified as potential candidates. Additionally it was subjected to ADME analysis using Swiss ADME software. Every contender demonstrated greater binding affinity and actively crossed the BBB. Protein–ligand complexes were subjected to molecular dynamics (MD) simulations using CABS-flexV2.0 and the iMOD server in order to assess the root-mean-square fluctuations (RMSFs) and quantify protein stability respectively. Hence, it is concluded that compounds with higher binding affinity and actively cross BBB values showed effective anti-encephalitis agents. We have performed molecular docking studies, ADMET analysis and MD simulation of all selected compounds and found that compounds G5, G17, G48, G15, G3 and G18 have showed better binding score against TNF-α in contrast to standard drug Acyclovir.