Dear Yahya,
Thank you for your insightful suggestion regarding the active site prediction tool from the Supercomputing Facility for Bioinformatics & Computational Biology at IIT Delhi. I appreciate your thoroughness in exploring the potential utility of this tool.
I'm glad to make your attention to the paper by Tanya Singh, D. Biswas, and B. Jayaram titled "AADS - An automated active site identification, docking and scoring protocol for protein targets based on physico-chemical descriptors." The fact that this paper has been cited by 181 researchers highlights its significance in the field. While I haven't personally used the server yet. Though a humble suggestion to leverage it for gaining a preliminary understanding of the active site of proteins of interest is intriguing.
I would love to suggest an approach of cross-checking the tool's predictions with known active sites of a minimum of 10 proteins from the same organism is a thoughtful idea. This comparative validation would indeed provide valuable insights into the reliability and accuracy of the tool's predictions. If the results align well with established knowledge, it would lend substantial credibility to the tool's effectiveness.
Considering the extensive citations of the paper and the potential of the tool, I believe that exploring its feasibility is a worthwhile endeavor. The ability to gain a preliminary idea about the active site of proteins of interest could greatly inform and streamline any research efforts.
Additionally, there are several other active site prediction tools available that you might find useful for your research. Here are a few notable options:
CASTp (Computed Atlas of Surface Topography of Proteins), SiteMap, LIGSITE, DoGSiteScorer, PockDrug-Server, etc
Hope this answer will be helpful for you.