Jarvis-Patrick method

Shemetulskis et al. [44] describe a method based on clustering that was used to compare two external databases with a corporate database. Each database was clustered independently using the Jarvis-Patrick method [46] representative subsets of each database were chosen and the subsets were then mixed and re-clustered. The number of clusters that contain compounds from only one of the databases was then used as an indication of the degree of overlap between the two databases. A limitation of this approach is the computational effort required to re-cluster the mixed subsets. 

Figure 3. Algorithm for the Jarvis-Patrick clustering method...

The Jarvis-Patrick method involves the use of a list of the top K nearest neighbours for each molecule in a dataset, i.e., the ATmolecules that are most similar to it. Once these lists have been produced for each molecule in the dataset that is to be processed, two molecules are clustered together if they are nearest neighbours of each other and if they additionally have some... 

The Jarvis-Patrick method requires O(N ) time and 0(N) space. 

The widespread application of the Jarvis-Patrick nonhierarchical method exists in part because of the influence of the publications by Willett et but also because of the availability of the efficient commercial... 

Nonhierarchical methods such as that described by Jarvis and Patrick (61) have been widely used for compound selection from large databases (62). The principle behind the Jarvis-Patrick method is to group together compounds that have a large number of nearest neighbors in common. However, the... 

Clustering has been used for many years for generating diverse compound sets for screening [24,25], It is a two-step process where, firstly, the dataset is divided into clusters so that molecules in the same cluster are similar whereas molecules in different clusters are dissimilar. Secondly, a diverse, or representative, set of compounds is chosen by selecting one or more compounds from each cluster. Thus, clustering is based on calculating the pairwise similarities between all molecules in the dataset. Many different clustering methods have been developed the most commonly used for chemical applications are the Jarvis Patrick and Ward s methods [25]. 

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