Diversity and Chemical Library Networks of Large Data Sets.

The quantification of chemical diversity has many applications in drug discovery, organic chemistry, food, and natural product chemistry, to name a few. As the size of the chemical space is expanding rapidly, it is imperative to develop efficient methods to quantify the diversity of large and ultralarge chemical libraries and visualize their mutual relationships in chemical space. Herein, we show an application of our recently introduced extended similarity indices to measure the fingerprint-based diversity of 19 chemical libraries typically used in drug discovery and natural products research with over 18 million compounds. Based on this concept, we introduce the Chemical Library Networks (CLNs) as a general and efficient framework to represent visually the chemical space of large chemical libraries providing a global perspective of the relation between the libraries. For the 19 compound libraries explored in this work, it was found that the (extended) Tanimoto index offers the best description of extended similarity in combination with RDKit fingerprints. CLNs are general and can be explored with any structure representation and similarity coefficient for large chemical libraries.

Citation

Timothy B Dunn, Gustavo M Seabra, Taewon David Kim, K Eurídice Juárez-Mercado, Chenglong Li, José L Medina-Franco, Ramón Alain Miranda-Quintana. Diversity and Chemical Library Networks of Large Data Sets. Journal of chemical information and modeling. 2022 May 09;62(9):2186-2201

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PMID: 34723537

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