Background: Virtual screening methods start to be well established as effective approaches to identify hits, candidates and leads for drug discovery research. Among those, structure based virtual screening (SBVS) approaches aim at docking collections of small compounds in the target structure to identify potent compounds. For SBVS, the identification of candidate pockets in protein structures is a key feature, and the recent years have seen increasing interest in developing methods for pocket and cavity detection on protein surfaces.

Identification of chemical compounds with specific biological activities is an important step in both chemical biology and drug discovery. When the structure of the intended target is available, one approach is to use molecular docking programs to assess the chemical complementarity of small molecules with the target; such calculations provide a qualitative measure of affinity that can be used in virtual screening (VS) to rank order a list of compounds according to their potential to be active. rDock is a molecular docking program developed at Vernalis for high-throughput VS (HTVS) applications. Evolved from RiboDock, the program can be used against proteins and nucleic acids, is designed to be computationally very efficient and allows the user to incorporate additional constraints and information as a bias to guide docking. This article provides an overview of the program structure and features and compares rDock to two reference programs, AutoDock Vina (open source) and Schrödinger's Glide (commercial). In terms of computational speed for VS, rDock is faster than Vina and comparable to Glide. For binding mode prediction, rDock and Vina are superior to Glide. The VS performance of rDock is significantly better than Vina, but inferior to Glide for most systems unless pharmacophore constraints are used; in that case rDock and Glide are of equal performance. The program is released under the Lesser General Public License and is freely available for download, together with the manuals, example files and the complete test sets, at http://rdock.sourceforge.net/

Druggability predictions are important to avoid intractable targets and to focus drug discovery efforts on sites offering better prospects. However, few druggability prediction tools have been released and none has been extensively tested. Here, a set of druggable and nondruggable cavities has been compiled in a collaborative platform ( http://fpocket.sourceforge.net/dcd ) that can be used, contributed, and curated by the community. Druggable binding sites are often oversimplified as closed, hydrophobic cavities, but data set analysis reveals that polar groups in druggable binding sites have properties that enable them to play a decisive role in ligand recognition. Finally, the data set has been used in conjunction with the open source fpocket suite to train and validate a logistic model. State of the art performance was achieved for predicting druggability on known binding sites and on virtual screening experiments where druggable pockets are retrieved from a pool of decoys. The algorithm is free, extremely fast, and can effectively be used to automatically sieve through massive collections of structures ( http://fpocket.sourceforge.net ).

Supplementary data are available at Bioinformatics online.