Plasma Protein Binding Predictor    

The study of how drugs bind to proteins is crucial in drug discovery. When a drug binds to proteins in plasma, it can limit the effectiveness of the drug. Plasma protein binding values (% fraction bound) are expressed as the percentage of the total plasma concentration of a drug that is bound to all plasma proteins.

In the Strand PPB model, developed by Strand Genomics, several machine-learning methods including neural networks, decision trees and support vector machines were employed to identify a small set from 1054 molecular descriptors that correlated with this pharmacokinetic parameter.

The input to the predictors is the 2-D structure of a molecule, which is used to compute the descriptors that are utilized by the models. Structures may be imported as either SMILES, MOL, SYBYL MOL2, or SD files.

 

Model Characteristics: Training and Cross Validation


Plasma Protein Binding Training, Cross Validation and Testing Statistics:

 
Classification Accuracy
Regression Accuracy
 
N
Low %
High %
% Accurately Predicted
R-squared
Training
306
99
100
85
0.93
Cross Validation
306
75
75
78
0.87
Testing
74
82
97
75
0.9

 
 
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