Protein-protein interactions (PPIs) are becoming highly attractive targets for drug discovery. Motivated
by the rapid accumulation of PPI data in public database and the success stories concerning the targeting of PPIs, a
machine-learning method based on sequence and structure properties was developed to access the druggability of
PPIs. Here, a comprehensive non-redundant set of 34 druggable and 122 less druggable PPIs were firstly presented
from the perspective of pockets. When tested by outer 5-fold cross-validation, the most representative model in
discriminating the druggable PPIs from the less-druggable ones yielded an average accuracy of 88.24% (sensitivity
of 82.38% and specificity of 92.00%). Moreover, a promising result was also obtained for the independent test set.
Compared to other methods, the method gives a comparative performance, which is most likely due to the construction
of a training set that encompasses less druggable PPIs and also the information of active pockets that have
evolved to bind a natural ligand.
Keywords: Active pockets, druggability, protein-protein interactions, sequence features, structure features, support vector machine.
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