PockDrug: A Model for Predicting Pocket Druggability That Overcomes Pocket Estimation Uncertainties

Predicting protein druggability is
a key interest in the target
identification phase of drug discovery. Here, we assess the pocket
estimation methods’ influence on druggability predictions by
comparing statistical models constructed from pockets estimated using
different pocket estimation methods: a proximity of either 4 or 5.5
Å to a cocrystallized ligand or DoGSite and fpocket estimation
methods. We developed PockDrug, a robust pocket druggability model
that copes with uncertainties in pocket boundaries. It is based on
a linear discriminant analysis from a pool of 52 descriptors combined
with a selection of the most stable and efficient models using different
pocket estimation methods. PockDrug retains the best combinations
of three pocket properties which impact druggability: geometry, hydrophobicity,
and aromaticity. It results in an average accuracy of 87.9% ±
4.7% using a test set and exhibits higher accuracy (∼5–10%)
than previous studies that used an identical apo set. In conclusion,
this study confirms the influence of pocket estimation on pocket druggability
prediction and proposes PockDrug as a new model that overcomes pocket
estimation variability.