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Investigation of chemical space networks using random matrix theory

Publication Type : Journal Article

Thematic Areas : Center for Computational Engineering and Networking (CEN)

Campus : Coimbatore

Center : Center for Computational Engineering and Networking

Verified : No

Year : 2022

Abstract : Large collections of molecules (chemical libraries) are nowadays routinely screened in the process of designing drugs for specific ailments. Chemical and structural similarities between these molecules can be quantified using molecular descriptors, and these similarities can in turn be used to represent any chemical library as an undirected network called a chemical space network (CSN). Here we study different CSNs using conventional graph measures as well as random matrix theory (RMT). For the conventional graph measures, we focus on the average degree, average path length, graph diameter, degree assortativity, transitivity, average clustering coefficient and modularity. For the RMT analyses, we examine the eigenvalue spectra of adjacency matrices constructed from the molecular similarities for different CSNs, and examine their local fluctuation properties, contrasting them with the predictions of RMT. Changes in the conventional graph measures and RMT statistics with the network structure are examined for three different chemical libraries by varying the edge density (fraction of the actual to the maximum possible number of edges) of the networks. It is found that the assortativity among the conventional graph measures, and long-range fluctuation statistics of RMT in eigenvalue space respond to the changes in global network structure as well as the chemical space. We expect that this investigation of the network characteristics of different kinds of chemical libraries will provide guidance in the design of high-throughput screening libraries for different drug design applications.

Cite this Research Publication : Manuja Kothiyal, Santosh Kumar, and N. Sukumar, “Investigation of chemical space networks using random matrix theory” Journal Math. Chemistry. 60, 891–914 (2022). DOI: 10.1007/s10910-022-01341-y

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