In-silico drug discovery of new potential NF-κB (nuclear factor-kappa B) inhibitors, using the computer aided drug design tools

Abstract

The Inhibition of IKK-β(nuclear factor kappa B kinase subunit beta),a specific modulator of NF-κB(nuclear factor-κB),is considered a valid target to discover new active compounds for various cancers and rheumatoid arthritis treatment.The aim of this work is twofold:Firstly, to develop new designed compounds IKK-β inhibitors using different computer aided drug design tools (QSAR, Molecular Docking,Molecular Dynamics and drug likeness evaluation).The analysis of the results of QSAR model and molecular docking succeeded to screen 21 interesting compounds with better inhibitory concentration having a good affinity to IKK-β. Secondly, is to develop new predictive QSAR models for the〖pIC〗_50 of of nuclear factor-κB(NF-κB)inhibitors prediction, based on machine learning methods.Based on the statistical analyses,it was found that the obtained[8.11.11.1]ANN model is reliable,robust and showed the better predictive ability compared to the MLR model.Thus,the [8.11.11.1]ANN model can be used quite satisfactorily for the prediction and screening of a new series of nuclear factor-κB(NF-κB) inhibitors.