Object recognition and localization via pose clustering
Computer Vision, Graphics, and Image Processing
Algorithms for the identifications of three-dimensional maximal common substructures
Journal of Chemical Information & Computer Sciences
RAPID: randomized pharmacophore identification for drug design
Computational Geometry: Theory and Applications - special issue on applied computational geometry
On the approximation of largest common subtrees and largest common point sets
Theoretical Computer Science
LEDA: a platform for combinatorial and geometric computing
LEDA: a platform for combinatorial and geometric computing
Privacy: A Machine Learning View
IEEE Transactions on Knowledge and Data Engineering
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Multiple semi-flexible 3d superposition of drug-sized molecules
CompLife'05 Proceedings of the First international conference on Computational Life Sciences
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We present a novel highly efficient method for the detection of a pharmacophore from a set of ligands/drugs that interact with a target receptor. A pharmacophore is a spatial arrangement of physicochemical features in a ligand that is responsible for the interaction with a specific receptor. In the absence of a known 3D receptor structure, a pharmacophore can be identified from a multiple structural alignment of the ligand molecules. The key advantages of the presented algorithm are: (a) its ability to multiply align flexible ligands in a deterministic manner, (b) its ability to focus on subsets of the input ligands, which may share a large common substructure, resulting in the detection of both outlier molecules and alternative binding modes, and (c) its computational efficiency, which allows to detect pharmacophores shared by a large number of molecules on a standard PC. The algorithm was extensively tested on a dataset of almost 80 ligands acting on 12 different receptors. The results, which were achieved using a standard default parameter set, were consistent with reference pharmacophores that were derived from the bound ligand-receptor complexes. The pharmacophores detected by the algorithm are expected to be a key component in the discovery of new leads by screening large drug-like molecule databases.