diversity_selection
Differences
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diversity_selection [2012/07/02 20:32] – [Basic options] rkiss | diversity_selection [2012/07/02 20:58] – [Algorithm] rkiss | ||
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* **Number of most diverse molecules**: | * **Number of most diverse molecules**: | ||
- | If you do not limit the selection, the full collection will be returned ordered by diversity. This means that the top N molecules in the resulting collection will be the most diverse N molecules. The //maximum similarity// | + | If you do not limit the selection, the full collection will be returned ordered by diversity. This means that the top N molecules in the resulting collection will be the most diverse N molecules. The //maximum similarity// |
==== Advanced options ==== | ==== Advanced options ==== | ||
- | You can adjust the meaning | + | Under Advanced options, you can adjust the definition |
- | * **Molecular descriptor**: | + | You will be able to set different similarity metrics as the measure of similarity. Currently, only the Tanimoto coefficient (Jaccard index)((http:// |
- | ==== Default options ==== | + | We plan to introduce more descriptors and more similarity measure types in the future. |
- | The default | + | * **Molecular |
- | If you have no suggestions to use another setup, you can rely on our choices. After implementation and evaluation of new fingerprints and metrics, the default setup can be changed. This can be tracked at the end of this document, in the Changelog section. | + | ==== Default options ==== |
+ | The default descriptor used is the linear fingerprint implemented in OpenBabel ((Open Babel v2.3.90 http:// | ||
==== Algorithm ==== | ==== Algorithm ==== | ||
- | We use an optimized implementation of the stepwise elimination algorithm((R. J. Taylor, J. Chem. Inf. Comput. Sci., 1995, 35, 59 67.)), which can be described as follows: | + | We use an optimized implementation of the stepwise elimination algorithm((R. J. Taylor, J. Chem. Inf. Comput. Sci., 1995, 35, 59-67.)), which can be described as follows: |
- | + | ||
- | - calculate the similarity matrix of the molecules in the input collection | + | |
- | - process the matrix elements as follows: | + | |
- | - select the largest off-diagonal element in the similarity matrix | + | |
- | - eliminate one molecule of the most similar molecule pair randomly | + | |
- | - go to step I. if off-diagonal elements remained | + | |
- | - sort the list of eliminated molecules by similarity values associated to the elimination steps in increasing order | + | |
- | During this process, | + | - Calculate |
+ | - Process the matrix elements as follows: | ||
+ | - Select the largest off-diagonal element | ||
+ | - Eliminate one molecule of the most similar molecule pair randomly | ||
+ | - Go to step I. if off-diagonal elements remained | ||
+ | - Sort the list of eliminated molecules by similarity values | ||
- | After the algorithm finishes, structures are sorted by similarity values and are placed in the result collection. The first molecules in the resulted | + | During this process, the size of the collection |
+ | After the algorithm finishes, structures are sorted by similarity values and are placed in the result collection. The first molecules in the resulted collection are the most dissimilar (most diverse) ones. The length of the result list is determined by input parameters: maximum number of compounds and similarity threshold. | ||
===== Limitations ===== | ===== Limitations ===== | ||