Combining Similarity Searching and Network Analysis for the Identification of Active Compounds

ACS Omega
Ryo Kunimoto, Jürgen Bajorath

Abstract

A variety of computational screening methods generate similarity-based compound rankings for hit identification. However, these rankings are difficult to interpret. It is essentially impossible to determine where novel active compounds might be found in database rankings. Thus, compound selection largely depends on intuition and guesswork. Herein, we show that molecular networks can substantially aid in the analysis of similarity-based compound rankings. A series of networks generated for rankings provides visual access to search results and adds chemical neighborhood and context information for reference compounds that are not available in rankings. Network structure is shown to serve as a diagnostic criterion for the likelihood to successfully select active compounds from rankings. In addition, comparison of different networks makes it possible to prioritize alternative similarity measures for search calculations and optimize the enrichment of active compounds in rankings.

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Citations

Jun 11, 2020·Phytochemical Analysis : PCA·Hua-Li ZuoYuan-Jia Hu
Aug 25, 2020·Acta Pharmaceutica Sinica. B·Lu LuoShilin Chen

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Software Mentioned

ChEMBL
iGraph R
house
Gephi
TcMCS
Tc
OEChem
CSNs

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