Selection Decision of the Scoring Functions in Molecular Docking

When you have to perform molecular docking, the selection of an appropriate scoring function becomes necessary and that depends on the specific research question and the availability of computational resources. Both empirical and physics-based scoring functions have their own strengths and weaknesses, and the choice of the scoring function will depend on the accuracy and speed required for the particular research question. What are scoring functions?

Empirical scoring functions are faster and less computationally expensive than physics-based scoring functions. They are based on a statistical analysis of known protein-ligand complexes and use a set of parameters to describe the interactions between the protein and the ligand. Empirical scoring functions have been shown to accurately predict the binding affinities of a wide range of ligands to diverse proteins.

Physics-based scoring functions, on the other hand, are based on physical principles such as force fields and quantum mechanics. They use computational models to simulate the interactions between the protein and the ligand and calculate the binding affinity. Physics-based scoring functions are more computationally expensive than empirical scoring functions, but they can provide more accurate predictions of the binding affinity.

In general, if the research question requires a high degree of accuracy in predicting the binding affinity, and if computational resources are available, a physics-based scoring function may be preferred. However, if speed and computational efficiency are important, an empirical scoring function may be more appropriate.

Ultimately, the selection of the appropriate scoring function will depend on careful consideration of the specific research question, the availability of computational resources, and the strengths and weaknesses of each type of scoring function.