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SAVI Space-combinatorial encoding of the billion-size synthetically accessible virtual inventory

  1. Author:
    Korn, Malte [ORCID]
    Judson, Philip [ORCID]
    Klein, Raphael
    Lemmen, Christian
    Nicklaus,Marc [ORCID]
    Rarey, Matthias [ORCID]

  2. Author Address

    University of Hamburg, ZBH - Center for Bioinformatics, 22761, Hamburg, Germany., Heather Lea, Bland Hill, Norwood, Harrogate, HG3 1TE, England., BioSolveIT GmbH, St. Augustin, Sankt Augustin, Germany., NCI, NIH, CADD Group, NCI-Frederick, Frederick, Maryland, 21702, USA., University of Hamburg, ZBH - Center for Bioinformatics, 22761, Hamburg, Germany. matthias.rarey@uni-hamburg.de.,
    1. Year: 2025
    2. Date: Jun 23
    3. Epub Date: 2025 06 23
  2. Journal: Scientific Data
    1. 12
    2. 1
    3. Pages: 1064
  4. Type of Article: Article
  5. Article Number: 1064
  1. Abstract:

    The Synthetically Accessible Virtual Inventory (SAVI) comprises a huge molecule collection. LHASA transform rules, originally intended for retro-synthetic analysis, were applied to Enamine Building Blocks in a forward synthetic manner. Adding new transforms, expressly developed for SAVI, resulted in SAVI-Lib-2020, a collection of more than a billion synthetically accessible compounds. Handling a billion molecules explicitly is computationally quite demanding for drug discovery applications. SAVI-Space-2024 was created to address this shortcoming. In this paper, we describe the design and implementation of SAVI-Space-2024. We emphasize its reaction-driven combinatorial data structure that encodes transformation rules as reaction SMARTS and applies them in a combinatorial manner. Based on Enamine Building Blocks, this approach yields 7.5 billion molecules while requiring only a fraction of the memory (1.4 GB compared to 210 GB). Furthermore, the improved search capabilities - including fast similarity and substructure searches and docking applications on standard hardware - represent a significant advance over the enumerated SAVI library. © 2025. The Author(s).

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External Sources

  1. View Full Text
  2. DOI: 10.1038/s41597-025-05384-z
  3. PMID: 40550819
  4. PMCID: PMC12185686
  5. PII : 10.1038/s41597-025-05384-z

Library Notes

  1. Fiscal Year: FY2024-2025
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