Name
Abstract | ||
|---|---|---|
The optimal overlap between two molecular structures is a useful measure of shape similarity. However, it usually requires significant computation. This work describes the design of shape-fingerprints: binary bit strings that encode molecular shape. Standard measures of similarity between two shape-fingerprints are shown to be an excellent surrogate for similarity based on volume overlap but several orders of magnitude faster to compute. Consequently, shape-fingerprints can be used for clustering of large data sets, evaluating the diversity of compound libraries, as descriptors in SAR and as a prescreen for exact shape comparison against large virtual databases. Our results show that a small set of shapes can be used to build these fingerprints and that this set can be applied universally. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1021/ci049651v | JOURNAL OF CHEMICAL INFORMATION AND MODELING |
Field | DocType | Volume |
Data mining,Orders of magnitude (numbers),Data set,Artificial intelligence,Cluster analysis,Small set,Binary number,Computation,ENCODE,Pattern recognition,Combinatorial chemistry,Small molecule,Mathematics | Journal | 45 |
Issue | ISSN | Citations |
3 | 1549-9596 | 13 |
PageRank | References | Authors |
0.78 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| James A. Haigh | 1 | 23 | 1.38 |
| Barry T. Pickup | 2 | 163 | 15.93 |
| J Andrew Grant | 3 | 177 | 16.57 |
| Anthony Nicholls | 4 | 464 | 39.97 |