Molecular docking is a popular way to screen for novel drug compounds. The method involves aligning small molecules to a protein structure and estimating their binding affinity. To do this rapidly for tens of thousands of molecules requires an effective representation of the binding region of the target protein. This paper presents an algorithm for representing a protein's binding site in a way that is specifically suited to molecular docking applications. Initially, the protein's surface is coated with a collection of molecular fragments that could potentially interact with the protein. Each fragment, or probe, serves as a potential alignment point for atoms in a ligand, and is scored to represent that probe's affinity for the protein. Probes are then clustered by accumulating their affinities, where high affinity clusters are identified as being the "stickiest" portions of the protein surface. The stickiest cluster is used as a computational binding "pocket" for docking. This method of site identification was tested on a number of ligand-protein complexes; in each case the pocket constructed by the algorithm coincided with the known ligand binding site. Successful docking experiments demonstrated the effectiveness of the probe representation.

IcePick is a system for computationally selecting diverse sets of molecules. It computes the dissimilarity of the surface-accessible features of two molecules, taking into account conformational flexibility. Then, the intrinsic diversity of an entire set of molecules is calculated from a spanning tree over the pairwise dissimilarities. IcePick's dissimilarity measure is compared against traditional 2D topological approaches, and the spanning tree diversity measure is compared against commonly used variance techniques. The method has proven easy to implement and is fast enough to be used in selection of reactants for numerous production-sized combinatorial libraries.