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How do I use GOLD to predict binding affinity?


GOLD has been optimised for the prediction of ligand binding positions rather than the prediction of binding affinities. It is not recommended to use scoring function values to suggest accurate binding affinities because many factors are ignored or approximated when calculating a docking score (protein and ligand motion, and accurate treatment of waters to name two).

Sometimes a correlation between fitness score and binding affinity can be observed. If so, care must be taken to ensure this is not artifactual. This usually occurs when an equally good correlation is found between binding affinity and molecular weight. Both fitness score and binding affinity tend to correlate with molecular weight to some extent.

It is possible to develop Quantitative Structure Activity Relationships (QSAR) between docking data and binding affinity using GoldMine. These can be used to predict binding affinity for new members in the same series.

For further information please refer to the GOLD and GoldMine user manuals.​ Please note that GoldMine has been superseded by the development of GOLD in the CSD Python API and is no longer supported.