Wednesday, November 04, 2009
Finding Key Members in Compound Libraries by Analyzing Networks of Molecules Assembled by Structural-Similarity
Author: Zsolt Lepp, Chunfei Huang, Takashi Okada
Source: J. Chem. Inf. Model. ASAP
This paper investigated the used of networks to analyze chemical libraries with regard of finding central key members and diversity. Finding central molecules in compound libraries is often performed by clustering based on structural similarity and the cluster centers correspond to the central structures. In the same way, molecular similarity networks were explored in this publication to identify the central nodes that correspond to the molecules in the central positions of the chemical space. To do this different types of molecular similarity (Russel-Rao, Tanimoto, Baroni-Urbani and Yule coefficients on fingerprint similarity) based networks were investigated, such as Minimum Spanning Trees, Threshold Network and various network layouts were investigated.
Using similarity and dissimilarity as the distance between the nodes, either central, similar compounds were identified (similarity) in the centers or most unique structures (dissimilarity).
While the visualization capability of the networks/trees is the most evident advantage to analyze molecular libraries, some of the minimum spanning tree method has also some drawbacks. In this network, only the minimum of connections are drawn, leading to a loss of information a all the network edges might contain important information. A solution for this problem is to create a threshold network, drawing edges that have a certain weight. Such use of molecular networks could be applied to clustering and virtual screening, library analysis and the selection of compound series.
On the example of adenosine antagonists, it was shown that network-based clustering can be effectively used for clustering compound libraries with a performance comparable or better than Ward clustering.
In conclusion the paper offers an interesting approach of applying graph layouts of molecular networks to visualize relationships among compounds and the hierarchical structure of a library. The studies were performed in the open soured Cytoscape format and a plugin was developed to integrate Marvin view to visualize structures (supporting information).
Source: J. Chem. Inf. Model. ASAP
This paper investigated the used of networks to analyze chemical libraries with regard of finding central key members and diversity. Finding central molecules in compound libraries is often performed by clustering based on structural similarity and the cluster centers correspond to the central structures. In the same way, molecular similarity networks were explored in this publication to identify the central nodes that correspond to the molecules in the central positions of the chemical space. To do this different types of molecular similarity (Russel-Rao, Tanimoto, Baroni-Urbani and Yule coefficients on fingerprint similarity) based networks were investigated, such as Minimum Spanning Trees, Threshold Network and various network layouts were investigated.
Using similarity and dissimilarity as the distance between the nodes, either central, similar compounds were identified (similarity) in the centers or most unique structures (dissimilarity).
While the visualization capability of the networks/trees is the most evident advantage to analyze molecular libraries, some of the minimum spanning tree method has also some drawbacks. In this network, only the minimum of connections are drawn, leading to a loss of information a all the network edges might contain important information. A solution for this problem is to create a threshold network, drawing edges that have a certain weight. Such use of molecular networks could be applied to clustering and virtual screening, library analysis and the selection of compound series.
On the example of adenosine antagonists, it was shown that network-based clustering can be effectively used for clustering compound libraries with a performance comparable or better than Ward clustering.
In conclusion the paper offers an interesting approach of applying graph layouts of molecular networks to visualize relationships among compounds and the hierarchical structure of a library. The studies were performed in the open soured Cytoscape format and a plugin was developed to integrate Marvin view to visualize structures (supporting information).
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