Implementation of nearest-neighbor searching in an online chemical structure search system

1986 ◽  
Vol 26 (1) ◽  
pp. 36-41 ◽  
Author(s):  
Peter Willett ◽  
Vivienne Winterman ◽  
David Bawden
Keyword(s):  
Chemical Structure ◽  
Nearest Neighbor ◽  
Search System ◽  
Nearest Neighbor Searching ◽  
Structure Search

scholarly journals KVGCN: A KNN Searching and VLAD Combined Graph Convolutional Network for Point Cloud Segmentation

2021 ◽  
Vol 13 (5) ◽  
pp. 1003
Author(s):  
Nan Luo ◽  
Hongquan Yu ◽  
Zhenfeng Huo ◽  
Jinhui Liu ◽  
Quan Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Nearest Neighbor ◽  
Semantic Segmentation ◽  
K Nearest Neighbor ◽  
Topological Graph ◽  
Convolutional Network ◽  
Cloud Data ◽  
Nearest Neighbor Searching ◽  
Point Cloud Segmentation ◽  
Local Feature Extraction

Semantic segmentation of the sensed point cloud data plays a significant role in scene understanding and reconstruction, robot navigation, etc. This work presents a Graph Convolutional Network integrating K-Nearest Neighbor searching (KNN) and Vector of Locally Aggregated Descriptors (VLAD). KNN searching is utilized to construct the topological graph of each point and its neighbors. Then, we perform convolution on the edges of constructed graph to extract representative local features by multiple Multilayer Perceptions (MLPs). Afterwards, a trainable VLAD layer, NetVLAD, is embedded in the feature encoder to aggregate the local and global contextual features. The designed feature encoder is repeated for multiple times, and the extracted features are concatenated in a jump-connection style to strengthen the distinctiveness of features and thereby improve the segmentation. Experimental results on two datasets show that the proposed work settles the shortcoming of insufficient local feature extraction and promotes the accuracy (mIoU 60.9% and oAcc 87.4% for S3DIS) of semantic segmentation comparing to existing models.

scholarly journals Identification of new superwarfarin-type rodenticides by structural similarity. The docking of ligands on the vitamin K epoxide reductase enzyme’s active site

2015 ◽  
Vol 7 (1) ◽  
pp. 108-122
Author(s):  
László Ferencz ◽  
Daniela Lucia Muntean
Keyword(s):  
Vitamin K ◽  
Chemical Structure ◽  
Enzyme Inhibitors ◽  
Structural Similarity ◽  
Vitamin K Epoxide Reductase ◽  
Pubchem Compound ◽  
Structure Search ◽  
Epoxide Reductase ◽  
Compound Database ◽  
Similarity Thresholds

Abstract The rodenticide brodifacoum is highly toxic to mammals and birds, and extremely toxic to fish. It is a highly cumulative poison due to its high lipophilicity and extremely slow elimination. For this reason, it may be interesting to find similar compounds in order to enlarge the spectrum of vitamin K epoxide reductase enzyme inhibitors used today in pest control. We used the Similar Compounds search type of the Chemical Structure Search of the PubChem Compound Database to locate records that are similar to the chemical structure of brodifacoum, using pre-specified similarity thresholds. Using the threshold ≥ than 95% for the similar structures criteria, we found 14 compounds (from over 30 million entries) that meet this criteria. Two of these compounds have a better binding affinity to vitamin K epoxide reductase enzyme than brodifacoum, but the binding energy of the other 12 substances is also high, having identical or lower lipophilicity; consequently, they will eliminate faster, possibly lacking a part of the adverse effects.

scholarly journals A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree

2021 ◽  
Vol 11 (20) ◽  
pp. 9581
Author(s):  
Wei Wang ◽  
Yi Zhang ◽  
Genyu Ge ◽  
Qin Jiang ◽  
Yang Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Index Structure ◽  
Spatial Indexing ◽  
Cloud Data ◽  
Neighbor Search ◽  
Nearest Neighbor Searching ◽  
Indexing Structure ◽  
Balanced Tree

The spatial index structure is one of the most important research topics for organizing and managing massive 3D Point Cloud. As a point in Point Cloud consists of Cartesian coordinates (x,y,z), the common method to explore geometric information and features is nearest neighbor searching. An efficient spatial indexing structure directly affects the speed of the nearest neighbor search. Octree and kd-tree are the most used for Point Cloud data. However, Octree or KD-tree do not perform best in nearest neighbor searching. A highly balanced tree, 3D R*-tree is considered the most effective method so far. So, a hybrid spatial indexing structure is proposed based on Octree and 3D R*-tree. In this paper, we discussed how thresholds influence the performance of nearest neighbor searching and constructing the tree. Finally, an adaptive way method adopted to set thresholds. Furthermore, we obtained a better performance in tree construction and nearest neighbor searching than Octree and 3D R*-tree.

Sign in / Sign up

Export Citation Format

Share Document