Progressive Docking: A Hybrid QSAR/Docking Approach for Accelerating In Silico High Throughput Screening

A combination of protein−ligand docking and ligand-based QSAR approaches has been elaborated, aiming
to speed-up the process of virtual screening. In particular, this approach utilizes docking scores generated
for already processed compounds to build predictive QSAR models that, in turn, assess hypothetical target
binding affinities for yet undocked entries. The “progressive docking” has been tested on drug-like substances
from the NCI database that have been docked into several unrelated targets, including human sex hormone
binding globulin (SHBG), carbonic anhydrase, corticosteroid-binding globulin, SARS 3C-like protease, and
HIV1 reverse transcriptase. We demonstrate that progressive docking can reduce the amount of computations
1.2- to 2.6-fold (when compared to traditional docking), while maintaining 80−99% hit recovery rates.
This progressive-docking procedure, therefore, substantially accelerates high throughput screening, especially
when using high accuracy (slower) docking approaches and large-sized datasets, and has allowed us to
identify several novel potent nonsteroidal SHBG ligands.