  Motivation: Prediction of ligands for proteins of known 3D structure is
important to understand structure-function relationship, predict molecular
function, or design new drugs. Results: We explore a new approach for ligand
prediction in which binding pockets are represented by atom clouds. Each target
pocket is compared to an ensemble of pockets of known ligands. Pockets are
aligned in 3D space with further use of convolution kernels between clouds of
points. Performance of the new method for ligand prediction is compared to
those of other available measures and to docking programs. We discuss two
criteria to compare the quality of similarity measures: area under ROC curve
(AUC) and classification based scores. We show that the latter is better suited
to evaluate the methods with respect to ligand prediction. Our results on
existing and new benchmarks indicate that the new method outperforms other
approaches, including docking. Availability: The new method is available at
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