Comparative In-Silico Screening Of Potent Peptides Lead Using Docking Strategy And AI Approaches For The Treatment Of Diabetes

Abstract

Diabetes mellitus is a disease of inadequate control of blood levels of glucose. Insulin resistance in impaired cell function is a major hallmark of type 2 diabetes, the currently standard medicine available for the treatment is Glimepiride or Metformin which generally causes VitaminB12 deficiency so prevent these types of things and make them more bio-friendly peptide-based drugs with help of AI (Machine Learning). Swiss ADME tool is used to predict physicochemical parameter evaluation and then evaluate toxicity by pro-tox II software and select those classes that lie between 5-6 and then checks receptor-ligand interaction by Swiss dock against SGLT-2 receptor (PDB code -7VSI). By docking result the most promising peptide is Cysteine-Tryptophan and Glycine-Leucine with ΔG value of -9.46 & -8.56 respectively followed by Cysteine-Threonine, Valine-Threonine in comparison to standard drug Glimepiride of ΔG value of -10.75 and according to SVM results Glycine class combination seems to be most promising. So, comparative study shows that there is some similarity to the man- made technique and Machine learning results.