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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月23日

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  • Malte Korn,
  • Philip Judson,
  • Raphael Klein,
  • Christian Lemmen,
  • Marc C. Nicklaus,
  • Matthias Rarey

摘要

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.