Ensemble docking is an inexpensive and widely used methodology to account for receptor flexibility in Structure-based virtual screening (SBVS). During the ensemble docking campaign, each ligand is docked to a set of rigid conformations of the target receptor. As a result, multiple docking scores are computed per ligand. However, there is still no agreement on how to combine the ensemble docking scores to obtain the final ligand ranking. A common alternative to aggregate these results is the use of consensus strategies. However, these strategies exhibit slight improvement regarding the single-conformation approach. Here, we evaluated the use of machine learning methodologies over the ensemble docking results to enhance the predictive power of SBVS. We selected two targets as study cases: the CDK2 and FXa proteins. The conformational ensembles of these proteins were built from their available crystallographic structures. The compound library included compounds from three benchmarking datasets (DUD, DEKOIS2, CSAR-2012) and from cocrystallized molecules. Ensemble Docking was performed using smina/Vinardo, and the results were processed through 30x4 cross-validation to train and evaluate two machine learning (ML) classifiers: Logistic Regression and Gradient Boosting Trees. Then, we statistically compared the ML classifiers' performance with that of the traditional consensus strategies. We also explored the effect of protein ensemble sizes and conformational selection criteria on ML models' performance. Our results indicate that the ML classifiers significantly outperform traditional consensus strategies and even the best performances achieved by smina/Vinardo with single-structure docking. We provided statistical evidence that supports the effectiveness of ML to improve the ensemble docking performance.

Combining ensemble docking and machine learning to improve structure-based virtual screening

ACS Fall 2021

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