computer-aided drug design and molecular dynamic simulations of inhibitors of some autoimmune disorder therapeutic targets

To tackle medication resistance in rheumatoid arthritis, type 1 diabetes, and grave’s disease, 32 compounds were chosen as new inhibitors of autoimmune disorders and underwent 2d-qsar, 3d-qsar, docking, admet, and molecular dynamics (md) simulation experiments. genetic approximation-multiple linear regression (ga-mlr) was used in the 2d-qsar investigation. the experimental activities and those obtained by model 1 were shown to have a respectable connection (r2 = 0.7616 and q2 = 0.6327). the structure-activity relationships (sar) were statistically studied using the 3d-qsar technique, which produced strong statistical significance for one high predictive model, comparative molecular field analysis (comfa: q2=0.785; r2=0.936; rext2= 0.818). the steric and electrostatic fields control the bioactivity, according to a thorough examination of the contour maps of the prediction models. this information is very useful in understanding the qualities that must be presented to create new and powerful inhibitors of autoimmune disorders. through these discoveries, 70 new inhibitors with improved receptor-targeting activity were designed. the last lead compounds were compound 32 and designed compound d40, which were found by virtual screening and subsequent molecular docking. compounds 32 and d40 have the ability to target proteins such as arginine deiminase 4 (pad4), major histocompatibility complex (mhc) class ii hla-dq-alpha chain, and thyrotropin receptor (or tsh receptor) proteins, according to the results of the md simulation for each protein-ligand complex. our studies suggest that compound 32 and designed compound d40 be studied in vitro and in vivo against some of the selected autoimmune disorders. the mm/gbsa binding free energies are also measured for the selected drugs. for pattern recognition, structural similarity, and hotspots binding energy prediction.