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Scoring functions for dockingIn the fields of computational chemistry and molecular modelling, scoring functions are approximate mathematical methods used to predict the strength of the non-covalent interaction (also referred to as binding affinity) between two molecules. Most commonly one of the molecules is a small organic compound such as a drug and the second is the drug's biological target such as a protein receptor.[1] Scoring functions have also been developed to predict the strength of other types of intermolecular interactions, for example between two proteins[2] or between protein and DNA.[3] Additional recommended knowledge
UtilityScoring functions are useful for:[4]
PrerequisitesScoring functions are normally parameterized (or trained) against a data set consisting of experimentally determined binding affinities between molecular species similar to the species that one wishes to predict. For predictions of affinities of ligands for proteins, the following must be known or predicted:
The information above yields the three dimensional structure of the complex. Based on this three dimensional structure, the scoring function can then estimate the strength of the association between the two partners in the complex using one of the methods outlined below. Finally the scoring function itself may be used to help predict both the binding mode and the active conformation of the small molecule binding partner in the complex. ClassesThere are three general classes of scoring functions:
Finally hybrid scoring functions have also been developed in which the terms from two or more of the above types of scoring functions are combined into one function. References
Categories: Molecular modelling | Computational chemistry | Protein structure |
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This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "Scoring_functions_for_docking". A list of authors is available in Wikipedia. |