Suppose we have 3 structurally similar ligands and we performed docking via 2 different algorithms of the same software. Is there any way to apply Bio-stats on the results?
Well, you can check how many different poses you get and how well the poses deviate from each other (considering both algorithms) from a perfect fit. And you can use inhibition values (or other activity measurements) and the scoring values to rank and evaluate your results (if you don't have activity values for the structurally similar ligands you can use others with known data as benchmark).
I think we can do, but if you apply stats you will get less mean value than the first good confirmation. So that is not appropriate i think so.
Well, you can check how many different poses you get and how well the poses deviate from each other (considering both algorithms) from a perfect fit. And you can use inhibition values (or other activity measurements) and the scoring values to rank and evaluate your results (if you don't have activity values for the structurally similar ligands you can use others with known data as benchmark).
How well do the algorithms reproduce a known crystal structure conformation? How reproducible are the conformations between different runs using the same algorithm? Which of the conformations produced by the different algorithms can be used to reasonable explain the existing SAR between the three ligands?
But can we apply distribution biostat so that we get a certain type of distribution and can be concluded further in terms of most expected or interactive amino acids.
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