Bootstrapping does not, assumes that the only thing you know about the variable distribution is the values you actually have. Select random rows from table, generate best model for that data. Repeat several times, saving each model, then combine all of them to generate a final model. Recognition at active site, not biological potency, is factor to consider. Want to deduce minimal recognition requirements to understand how a diverse set of chemical structures can activate the same receptor.
Technique used is manipulation of orientation maps.
Method to find min or max of function that depends on many variables. Run a long dynamics simulation, while gradually lowering the temperature. Energetically excited molecule should then cool into a favorable energy well which corresponds to a local energy minimum in conformational space. Typically do many runs, and observe where final structures are clustering. Therefore error is minimized.