Objective:
To continue work on the 3D Shape searching technology for searching for parts across a manufacturer’s supply chain and for retrieval and classification from a Structural Bioinformatics Database

Background:
3D objects are widespread and used in many diverse areas such as computer graphics, computer aided design, computer vision and cultural heritage, medical imaging, structural biology, and other fields. Large numbers of 3D models are created every day and many are stored in publicly available databases. Understanding the 3D shape and structure of these models is essential to many scientific activities. These 3D scientific databases require methods for storage, indexing, searching, clustering, retrieval, and recognition of the content under study. While there has been work done in the retrieval of text and 2D images, these methods simply can’t be extended to 3D data. 3D search requires surface-based and volume-based features or descriptors to effectively characterize the shape, semantics, and geometric topology. We plan to develop 3D shape searching technologies for: 1) for searching for CAD type parts across the manufacturer’s supply chain; and 2) for the emerging field of structural bioinformatics.

According to AutoDesk there are over 20 Billion CAD models, compared to 6 Billion people. Different estimates by experts put the number of unique designs of parts at around 60 to 800 Billion. Even a single Boeing Aircraft 787 has more than 3 million unique parts from different part suppliers. Using 3D shape searching early in the design cycle can detect duplicate parts and can also locate similar parts manufactured across your supply chain. Hence there will be cost saving associated parts reuse and avoiding duplicate parts to help identify and reuse their existing designs and manufacturing processes.

It is widely believed that the 3D shapes of macro molecules and their active sites provide a discriminating role in bio-molecular recognition and function. Geometrical shapes determine their ability to bind to their targets. Characterization of geometrical shape may thus provide information to classify and retrieve related and functionally relevant macro-molecules for purposes such as drug targeting. There are over 46,000 protein structures in the Protein Data Bank (PDB). These 3D structural databases pose challenges for storing, indexing, searching, clustering, retrieval of shape based structural information. Techniques used in text based retrieval of structural information may not be easily extended to shape based 3D or 2D searches that require surface-based and volume-based descriptors to effectively characterize the shape, semantics and geometric topology. Hence there is a need for an automated rule-based 3D retrieval and classification system to efficiently manage Structural Bioinformatics Databases. We have developed a shape based retrieval and classification method for a few of the structures taken from the PDB. The method involves developing 3D shape descriptors to describe the 3D shape of each structure. The shape descriptors that we have developed are based on histograms of distances between atoms, moments and Spherical harmonics of the surface of the molecules. We have used this method to develop proximity measures for structures that may be used for assessing their similarity.

 

Page created October 2007

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