Diversifying chemical libraries with generative topographic mapping

Abstract

Generative topographic mapping was used to investigate the possibility to diversify the in-house compounds collection of Boehringer Ingelheim (BI). For this purpose, a 2D map covering the relevant chemical space was trained, and the BI compound library was compared to the Aldrich-Market Select (AMS) database of more than 8M purchasable compounds. In order to discover new (sub)structures, the “AutoZoom” tool was developed and applied in order to analyze chemotypes of molecules residing in heavily populated zones of a map and to extract the corresponding maximum common substructures. A set of 401K new structures from the AMS database was retrieved and checked for drug-likeness and biological activity.

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Abbreviations

GTM:

Generative topographic mapping

FS:

Frame set

LLh:

Likelihood

AD:

Applicability domain

RBF:

Radial basis function

DB:

Database

AMS:

Aldrich Market Select

BI:

Boehringer Ingelheim

MCS:

Maximum common substructure

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Acknowledgements

The authors thank Boehringer Ingelheim Pharma GmbH & Co KG for the provided data.

Funding

The project leading to this article has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant Agreement No. 676434, “Big Data in Chemistry” (“BIGCHEM”, http://bigchem.eu).

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The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

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Correspondence to Bernd Beck or Alexandre Varnek.

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Lin, A., Beck, B., Horvath, D. et al. Diversifying chemical libraries with generative topographic mapping. J Comput Aided Mol Des 34, 805–815 (2020). https://doi.org/10.1007/s10822-019-00215-x

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Keywords

  • Generative topographic mapping
  • Chemical library diversity enrichment
  • Big data