The distribution of binding affinity is Gaussian around the mean and becomes exponential near the tail.

The power law behavior of the equilibrium constant in the tail therefore indicates that most of the binding molecules show small binding affinities , only occasionally a specific pattern will lead to high affinity.

Notice that due to the equivalence of the ensemble eXploring different ligands and ensemble exploring different interactions through different atomic contacts of specific ligands to receptor, the statistical distribution of the physical relevant quantity such as binding affinity from different ligands binding to the same receptor is equivalent to the distribution of the one obtained from different interactions of the specific ligand-receptor binding interactions through spatial atomic contacts(See Fig 1).

Therefore in analytical model, for example, we can obtain the distribution of binding affinity for the ensemble of different atomic contact interactions (with different nonnative unbinding states).

Validation of Autodock scoring to predict the binding affinities for 20 drugs against the Cox-2.

The conventional specificity refers to the discrimination of binding affinities between a specific ligand with different receptors.

From the above equilvalence discussions in terms of probing the interactions of molecular recognition, another way of quantify the specificity is to find the discrimination in binding affinities of a ligand binding with different binding sites of a receptor.

Thus, based on the fitted parameter from relatively small ligand database (about 700 members in the current study), which statistically attain binding affinities in the range of -4kcal/mol to -12kcal/mol; it is possible to derive information regarding the much higher binding affinities for larger libraries.

By starting with a training set containing approximately 100 peptides with their corresponding validated bioactivity ( binding affinity , IC50, etc), we expect that a state-of-the-art kernel method will give a bioactivity model which is sufficiently accurate to find new peptides with activities higher than the 100 used to learn the model.

This is possible because each peptide that possesses a small binding affinity contains information about subsequences of residues that can bind to the target.

This algorithm makes use of graph theory and recent work [14] on the prediction of the bioactivity and the binding affinity between peptides and a target protein.

Recently [14], the GS kernel was used to learn a predictor capable of predicting, With reasonable accuracy, the binding affinity of any peptide to any protein on the Per database.

The GS kernel has also outperformed current state-of-the-art methods for predicting peptide-protein binding affinities on single-target and pan-specific Major Histocompatibility Complex (MHC) class II benchmark datasets and three Quantitative Structure Affinity Model benchmark data-sets.

In the regression setting, the learning task is to predict a real value that quantifies the quality of a peptide, for example, its bioactiVity, inhibitory concentration, binding affinity , or bioavailability.

Using all-atom MD simulations to quantify perturbations to the PC190723-binding pocket due to re-sistance-inducing mutations, we have provided an indirect assessment of PC190723 binding affinity that can be used as a point of comparison for at least two relevant applications.

However, the significance of Pocket-FEATURE scores for binding affinity has only been loosely correlated.

Pocket similarity score predicts that polymerization increases PC190723 binding affinity .

This observation suggests that conformational changes associated with nucleotide binding may tune the binding affinity of PC190723 for FtsZ; no GTP-bound SaFtsZ structures exist, leaving it unclear whether GTP hydrolysis significantly affects pocket score.

The CLS corresponds to that step where the concentration of the accelerator is limited compared to its binding affinity but the free concentration of accelerators in reactions after the CLS are in excess compared to their bound concentrations.

However, a non-This form for mass conservation can be achieved if the concentration of X2 is limited with respect to its binding affinity , i.e., q2[X2] < <1, while the other factors are not limited.

Hence, the CLS is a step where the accelerator concentration is limited with respect to its binding affinity but the accelerator(s) following it are not limited.