In silico identification of prospective p53-MDM2 inhibitors from ASINEX database using a comprehensive molecular modelling approach.

Cancer cells have a higher evolutionary potential than normal cells, which commonly leads to medication resistance and a decrease in the efficacy of existing cancer treatments. As a result, discovering new therapeutic drugs is an important priority in the field of oncology. The tumor suppressor protein p53, regulates many cellular activities but is frequently rendered inactive in malignancies due to aberrant overproduction of MDM2 and MDMX. As a result, the method of targeting MDM2 with small-molecule inhibitors to reactivate p53 signalling has gained popularity as a promising approach for anticancer drug development. In this study, we performed a comprehensive structure-based virtual screening of 261,120 compounds from the Asinex database, along with molecular docking, ADMET profiling, and molecular dynamics (MD) simulations using Schrödinger's Maestro platform, to identify high-affinity MDM2 binders. electronic properties and stability characteristics have been assessed using Density Functional Theory (DFT) computations. The generated lead compounds had favourable pharmacokinetic features and high binding affinities for MDM2, making them suitable scaffolds for further therapeutic study. Overall, our findings lay the groundwork for experimental validation and drive the hunt for next-generation inhibitors of the p53-MDM2 pathway in cancer therapy.