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Compound collections at KU 1947–2017: cheminformatic analysis and computational protein target prediction

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Abstract

We report the comparison of two small-molecule collections synthesized at KU at two different eras. We used a machine learning tool to classify the compounds in these collections by their predicted protein targets. The analyses shine light on the evolution of medicinal chemistry research at the University of Kansas, and reveal several new associations between compounds and protein targets.

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Data availability

All the analyses, codes to generate figures, and SMILES data for the compounds in these collections can be found at our laboratory’s GitHub page associated with this paper: https://github.com/boskovicgroup/ccaptp.

Notes

  1. https://www.cureffi.org/2013/10/04/list-of-fda-approved-drugs-and-cns-drugs-with-smiles/.

  2. Mathematical requirements for a space are clearly not satisfied: it is not possible to linearly combine the members of the set, and there is no neutral element of the set.

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Acknowledgements

We want to thank the Environmental Health and Safety team at KU, specifically Jon Rossillon and Jennifer Phillips, for their help with transporting the collection across the KU campus. New Faculty General Research Fund, General Research Fund, and Protein Structure–Function COBRE supported our projects around the small-molecule collections. Special thanks go to all the unnamed researchers who contributed compounds to the collections we have described.

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Authors and Affiliations

  1. Department of Medicinal Chemistry, University of Kansas, Lawrence, KS, 66045, USA

    Zachary Pearson, Manvendra Singh & Zarko Boskovic

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  1. Zachary Pearson
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  2. Manvendra Singh
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  3. Zarko Boskovic
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Correspondence to Zarko Boskovic.

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Dedication: This paper honors Prof. Robert Hanzlik and his retirement following 49 years at the University of Kansas.

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Pearson, Z., Singh, M. & Boskovic, Z. Compound collections at KU 1947–2017: cheminformatic analysis and computational protein target prediction. Med Chem Res 29, 1211–1222 (2020). https://doi.org/10.1007/s00044-020-02571-y

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  • DOI: https://doi.org/10.1007/s00044-020-02571-y

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