Insights into SIRT2 inhibition from machine learning-assisted multi-level screening of the NCI database

values of -74.3, -73.1, -71.5, and -47.8 kcal/mol, respectively. Post-MD analyses consistently supported the promising stability and binding profiles of the identified NCI compounds bound to SIRT2 throughout 300 ns MDS. The physicochemical and ADMET features of the identified NCI compounds were predicted, indicating their favorable oral bioavailability and pharmacokinetic profiles. Eventually, DFT computations were executed to assess the chemical reactivity of the identified NCI compounds. Collectively, these findings highlighted NCI243049, NCI407129, and NCI248613 as promising SIRT2 inhibitors, meriting further validation through experimental assays for cancer therapy.