diversity_selection
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- | ====== Diversity selection ====== | ||
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- | This filter selects the most diverse compounds from large compound collections by eliminating of the most similar structures. The size of the input collection is decreased, while the maximum possible coverage of its represented chemical space is retained. | ||
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- | ===== When to use ===== | ||
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- | If you have limited experimental or computational resources, diversity selection is an unbiased way to limit the number of compounds to handle. Collecting compounds from different regions of the chemical space is an efficient strategy to maximize the diversity of the identified active scaffolds. | ||
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- | Using this filter you can either reduce the size of large (virtual) screening libraries, or select a diverse, representative set of your virtual hits. | ||
- | ===== How to use ===== | ||
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- | It is recommended to apply structural or phys-chem property filters prior to diversity selection to eliminate unwanted structures. This is important to avoid exotic structures that are typically identified as very diverse by the algorithm. | ||
- | ==== Basic options ==== | ||
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- | The following options can be used to control the diversity of the resulting collection. | ||
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- | * **Similarity threshold**: | ||
- | * **Number of most diverse molecules**: | ||
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- | 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// | ||
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- | ==== Advanced options ==== | ||
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- | Under Advanced options, you can adjust the definition of similarity/ | ||
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- | You will be able to set different similarity metrics as the measure of similarity. Currently, only the Tanimoto coefficient (Jaccard index)((http:// | ||
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- | We plan to introduce more descriptors and more similarity measure types in the future. | ||
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- | * **Molecular descriptor**: | ||
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- | ==== Default options ==== | ||
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- | The default descriptor used is the linear fingerprint implemented in OpenBabel ((Open Babel v2.3.90 http:// | ||
- | ==== Algorithm ==== | ||
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- | 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: | ||
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- | - 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 | ||
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- | During this process, the size of the collection is reduced and diversity increases. Each elimination step throws out a compound that has close analogues in the remaining set. In result, we get a single compound, and a list of compounds with decreasing similarity values, which can be interpreted as the increasing diversity of the remaining set. | ||
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- | 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, and the length of the result list is determined using the diversity options: the maximum number of compounds to be selected and the maximum similarity values allowed between diverse compounds. | ||
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- | ===== Limitations ===== | ||
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- | The diversity selection is freely accessible for every mcule user with a monthly limit of 10000 input compounds. The average run time for 10000 compouds is about 5 minutes. The usage of your diversity filter can be tracked on the user profile / limits. Our technologies allow effective processing of very large collections (~10M). If you want to exceed your limits, please contact us. | ||
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- | ===== Changelog ===== | ||
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- | --- | ||
diversity_selection.1341262238.txt.gz · Last modified: 2012/07/02 20:50 by rkiss