Recent successes in virtual screening have been made possible by large models
and extensive chemical libraries. However, combining these elements is
challenging: the larger the model, the more expensive it is to run, making
ultra-large libraries unfeasible. To address this, we developed a
target-agnostic, efficacy-based molecule search model, which allows us to find
structurally dissimilar molecules with similar biological activities. We used
the best practices to design fast retrieval system, based on
processor-optimized SIMD instructions, enabling us to screen the ultra-large
40B Enamine REAL library with 100\% recall rate. We extensively benchmarked our
model and several state-of-the-art models for both speed performance and
retrieval quality of novel molecules.

通过开发目标不确定、以效能为基础的分子搜索模型，我们能在具有类似生物活性的分子中找到结构不相似的分子。我们使用最佳实践设计了一种快速检索系统，通过处理器优化的 SIMD 指令，可以在 100% 的召回率下筛选超大型的 Enamine REAL 化合物库，并对该模型及其他最先进模型进行了广泛的性能和检索质量的基准测试。