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A Fast Flexible Docking Method using an Incremental Construction Algorithm
 J. Mol. Biol
, 1996
"... We present an automatic method for docking organic ligands into protein Center for Information binding sites. The method can be used in the design process of specific Technology (GMD), Institute protein ligands. It combines an appropriate model of the physicochemical for Algorithms and Scientific p ..."
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Cited by 97 (2 self)
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We present an automatic method for docking organic ligands into protein Center for Information binding sites. The method can be used in the design process of specific Technology (GMD), Institute protein ligands. It combines an appropriate model of the physicochemical for Algorithms and Scientific properties of the docked molecules with efficient methods for sampling the Computing (SCAI), Schloß conformational space of the ligand. If the ligand is flexible, it can adopt
Parallel Algorithms for Hierarchical Clustering
 Parallel Computing
, 1995
"... Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces. O(n 2 ) algorithms are known for this problem [3, 4, 10, 18]. This paper reviews important results for sequential algorithms and describes previous work on parallel algorithms f ..."
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Cited by 80 (1 self)
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Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces. O(n 2 ) algorithms are known for this problem [3, 4, 10, 18]. This paper reviews important results for sequential algorithms and describes previous work on parallel algorithms for hierarchical clustering. Parallel algorithms to perform hierarchical clustering using several distance metrics are then described. Optimal PRAM algorithms using n log n processors are given for the average link, complete link, centroid, median, and minimum variance metrics. Optimal butterfly and tree algorithms using n log n processors are given for the centroid, median, and minimum variance metrics. Optimal asymptotic speedups are achieved for the best practical algorithm to perform clustering using the single link metric on a n log n processor PRAM, butterfly, or tree. Keywords. Hierarchical clustering, pattern analysis, parallel algorithm, butterfly network, PRAM algorithm. 1 In...
How easy is matching 2D line models using local search?
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1997
"... Local search is a well established and highly effective method for solving complex combinatorial optimization problems. Here, local search is adapted to solve difficult geometric matching problems. Matching is posed as the problem of finding the optimal manytomany correspondence mapping between a ..."
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Cited by 28 (3 self)
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Local search is a well established and highly effective method for solving complex combinatorial optimization problems. Here, local search is adapted to solve difficult geometric matching problems. Matching is posed as the problem of finding the optimal manytomany correspondence mapping between a line segment model and image line segments. Image data is assumed to be fragmented, noisy, and cluttered. The algorithms presented have been used for robot navigation, photo interpretation, and scene understanding. This paper explores how local search performs as model complexity increases, image clutter increases, and additional model instances are added to the image data. Expected runtimes to find optimal matches with 95 percent confidence are determined for 48 distinct problems involving six models. Nonlinear regression is used to estimate runtime growth as a function of problem size. Both polynomial and exponential growth models are fit to the runtime data. For problems with random clutter, the polynomial model fits better and growth is comparable to that for tree search. For problems involving symmetric models and multiple model instances, where tree search is exponential, the polynomial growth model is superior to the exponential growth model for one search algorithm and comparable for another.
Efficient Pose Clustering Using a Randomized Algorithm
, 1997
"... . Pose clustering is a method to perform object recognition by determining hypothetical object poses and finding clusters of the poses in the space of legal object positions. An object that appears in an image will yield a large cluster of such poses close to the correct position of the object. If t ..."
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Cited by 23 (6 self)
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. Pose clustering is a method to perform object recognition by determining hypothetical object poses and finding clusters of the poses in the space of legal object positions. An object that appears in an image will yield a large cluster of such poses close to the correct position of the object. If there are m model features and n image features, then there are O(m 3 n 3 ) hypothetical poses that can be determined from minimal information for the case of recognition of threedimensional objects from feature points in twodimensional images. Rather than clustering all of these poses, we show that pose clustering can have equivalent performance for this case when examining only O(mn) poses, due to correlation between the poses, if we are given two correct matches between model features and image features. Since we do not usually know two correct matches in advance, this property is used with randomization to decompose the pose clustering problem into O(n 2 ) problems, each of which...
Lengauer T, Timeefficient docking of flexible ligands into active sites of proteins
 Proc Int Conf Intell Syst Mol Biol, 3
, 1995
"... We present ~ algorithm for placing flexible molecules in active sites of proteins. The two major goals in the development of our docking program, called FLExX, are the explicit exploitation of molecular flexibility of the li$~nd ~nd the development of a model of the docking process that includes the ..."
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Cited by 11 (1 self)
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We present ~ algorithm for placing flexible molecules in active sites of proteins. The two major goals in the development of our docking program, called FLExX, are the explicit exploitation of molecular flexibility of the li$~nd ~nd the development of a model of the docking process that includes the physicochemical properties of the molecules. The algorithm consists of three phases: The selection of a base fragment, the placement of the base fragment in the active site, a~d the incremental construction of the ligand inside the active site. Except for the selection of the base fragment, the algorithm runs without manual intervention. The algorithm is tested by reproducing 11 receptorligand complexes known from Xray crystallography. In all cases, the algorithm predicts a placement of the ligand which is similar to the crystal structure (about 1.5 RMS deviation or less) in a few minutes on a workstation, assuming that the receptor is given in the bound conformation.
Recognition by Matching Dense, Oriented Edge Pixels
 In Proceedings of the International Symposium on Computer Vision
, 1995
"... This paper describes techniques to perform efficient and accurate recognition in difficult domains by matching dense, oriented edge pixels. We model threedimensional objects as the set of twodimensional views of the object. Translation, rotation, and scaling of the views are allowed to approximate ..."
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Cited by 5 (3 self)
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This paper describes techniques to perform efficient and accurate recognition in difficult domains by matching dense, oriented edge pixels. We model threedimensional objects as the set of twodimensional views of the object. Translation, rotation, and scaling of the views are allowed to approximate full threedimensional motion. A modified Hausdorff measure is used to determine which transformations of each object model are reported as matches. The use of dense, oriented edge pixels allows us to achieve a low rate of false positives. Techniques to prune the search space are used to obtain a system that is efficient in practice. We give results of the system recognizing object views in intensity and infrared images. 1 Introduction Much recent work on object recognition has focused on matching sparse feature points in the object model and in the image. Analysis has shown that relying on such feature points implies that false positives will occur in images with moderate complexity [4, 5, ...
Retrospect and Prospect of Virtual Screening in Drug Discovery
"... Abstract: We review the prominent technologies in virtual screening, and their applications in drug discovery. ..."
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Cited by 2 (0 self)
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Abstract: We review the prominent technologies in virtual screening, and their applications in drug discovery.
Probabilistic Indexing: A New Method of Indexing 3D Model Data from 2D Image Data
 In Proceedings of the Second CADBased Vision Workshop
, 1994
"... Recent research has indicated that indexing is a promising approach to fast modelbased object recognition because it allows most of the possible matches between image point groups and model point groups to be quickly eliminated from consideration. Current indexing systems for the problem of recogni ..."
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Cited by 1 (1 self)
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Recent research has indicated that indexing is a promising approach to fast modelbased object recognition because it allows most of the possible matches between image point groups and model point groups to be quickly eliminated from consideration. Current indexing systems for the problem of recognizing general 3D objects from single 2D images require groups of four points to generate a key into the index table and each model group requires many entries in the table. I present a system that is capable of indexing using groups of three points by taking advantage of the probabilistic peaking effect [1]. Each model group need only be represented at one point in the index table. The ability to index using groups of three points means that there are many fewer model groups and image groups to consider, but to be able to index using groups of three points, false negatives matches must be allowed. We can withstand these false negatives by examining information from multiple groups. Results ...
Fast Object Recognition by Selectively Examining Hypotheses
, 1994
"... Several systems have been proposed to recognize threedimensional objects in (twodimensional) intensity images by computer. A problem that has plagued most object recognition systems for this problem is the low rate at which images are processed unless the problem is constrained, due to the vast ..."
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Cited by 1 (0 self)
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Several systems have been proposed to recognize threedimensional objects in (twodimensional) intensity images by computer. A problem that has plagued most object recognition systems for this problem is the low rate at which images are processed unless the problem is constrained, due to the vast number of hypothetical matches between sets of image features and sets of model features. Hypothetical poses can be determined from a small number of model features appearing in the image. The number of correct matches between these small sets of model features and image features (and thus correct hypotheses) is combinatorial in the number of model features appearing in the image. Since, ideally, only one of these correct hypotheses needs to be found to recognize the object, an exhaustive examination of ...