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On the other hand, MM-GBSA is normally used in the computation of the free binding energy differences between the bound and the unbound states of solvated molecules. This paper seeks to critically discuss the MM-GB (PB) SA methods and their potential applications There are a number of alternative and competing methods to MM-GB (PB) some of which include free energy perturbation (FEP), multi-state Bennett acceptance ratio (MBAR) and thermodynamic integration (TI) among others [1]. Many people use these alternative methods due to their computational accuracy.

However, compared to the other methods, MM-GBSA and MM-PBSA methods are more computationally efficient molecular modeling algorithms that are potentially quite useful in drug design particularly with regard to ranking drug binding affinity. Drug binding affinity ranking is critically important in computer aided drug design where it is normally used to facilitate the efficiency and accuracy of the routine identification of the possible candidates. This is particularly critical during the early stage stages of drug discovery [1].

Generally, Implicit solvents addresses the problem by representing solvent as a continuous medium as opposed to individual “explicit” solvent molecules in order to estimate free energy of solute-solvent interactions in structural and chemical processes some of which often include folding and conformational transitions of proteins. This can significantly help in the estimation of the contribution of each residue to the overall protein-ligand/protein binding; thereby helping in the identification of mutations that can potentially enhance the binding affinities of the protein complex.

A number of previous researches have explored the potential reliability of using MM-GB(PB)SA in estimating ligand binding affinities of a series of structurally diverse inhibitors. On the other hand,