Algorithm of NFkBIn

NF-kB (nuclear factor kappa B) inhibitors are compounds or molecules designed to block the activity of NF-kB, a transcription factor involved in regulating immune response, inflammation, and cell survival. NF-kB is activated in response to various stimuli, including stress, cytokines, and pathogens, and it plays a crucial role in the expression of genes associated with inflammation and immune responses. NF-kB is a central player in numerous cellular processes and diseases, making it a critical target for therapeutic intervention and a focal point of research in immunology, oncology, and chronic disease management.

The prediction server NF-kB inhibitor molecules prediction has been designed in a very user-friendly manner. Here, on this page, user can get the details of all the algorithms and procedures exploited in the different modules.
This algorithm of the server is relies on the following three models:

Prediction model

This module has been developed to predict the chemical molecules as Signal Transducer and Activator of TNF alpha induced NF-kB inhibitor or non-inhibitor. Here the users are allowed to paste or upload a file in SMILES format, and each molecule would be predicted as inhibitor or non-inhibtor of NF-kB based on the selected threshold value. The best Machine learning model developed for screen of NF-kB inhibitors have been incorporated as default paramenters.

Draw model

This module allows users to draw the chemical structure of the molecule using the Ketcher. Users have the choice to either build a new molecule or edit/modify an existing molecule. To facilitate users, an example structure is provided that can be loaded in the Ketcher by clicking on "Load Example" button. This module enables users to classify the drawn chemical structure as NF-kB inhibitor or not.

Analog Design model

This module is designed specifically for the users who want to develop analogs of their lead molecule. A user needs to submit a scaffold structure along with the building blocks and linker molecules. Using the submitted information, the web server uses the SmiLib package at the backend and links the building blocks (fragments) to the scaffold using linker to generate all the possible analogs. Each analog is further predicted as STAT3 inhibitor and non-inhibitor, and the results are displayed in tabular form.