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Motivation: Prediction of ligands for proteins of known 3D structure isimportant to understand structure-function relationship, predict molecularfunction, or design new drugs. Results: We explore a new approach for ligandprediction in which binding pockets are represented by atom clouds. Each targetpocket is compared to an ensemble of pockets of known ligands. Pockets arealigned in 3D space with further use of convolution kernels between clouds ofpoints. Performance of the new method for ligand prediction is compared tothose of other available measures and to docking programs. We discuss twocriteria to compare the quality of similarity measures: area under ROC curve(AUC) and classification based scores. We show that the latter is better suitedto evaluate the methods with respect to ligand prediction. Our results onexisting and new benchmarks indicate that the new method outperforms otherapproaches, including docking. Availability: The new method is available athttp://cbio.ensmp.fr/paris/ Contact: mikhail.zaslavskiy@mines-paristech.fr

A new protein binding pocket similarity measure based on comparison of 3D atom clouds: application to ligand prediction