BitBIRCH: efficient clustering of large molecular libraries†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY
Kenneth López Pérez, Vicky Jung, Lexin Chen, Kate Huddleston and Ramón Alain Miranda-Quintana
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引用次数: 0

Abstract

The widespread use of Machine Learning (ML) techniques in chemical applications has come with the pressing need to analyze extremely large molecular libraries. In particular, clustering remains one of the most common tools to dissect the chemical space. Unfortunately, most current approaches present unfavorable time and memory scaling, which makes them unsuitable to handle million- and billion-sized sets. Here, we propose to bypass these problems with a time- and memory-efficient clustering algorithm, BitBIRCH. This method uses a tree structure similar to the one found in the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm to ensure O(N) time scaling. BitBIRCH leverages the instant similarity (iSIM) formalism to process binary fingerprints, allowing the use of Tanimoto similarity, and reducing memory requirements. Our tests show that BitBIRCH is already >1000 times faster than standard implementations of the Taylor–Butina clustering for libraries with 1 500 000 molecules. BitBIRCH increases efficiency without compromising the quality of the resulting clusters. We explore strategies to handle large sets, which we applied in the clustering of one billion molecules under 5 hours using a parallel/iterative BitBIRCH approximation.

Abstract Image

机器学习(ML)技术在化学应用中的广泛应用,带来了分析超大分子库的迫切需求。特别是,聚类仍然是剖析化学空间的最常用工具之一。不幸的是,大多数当前的方法都存在不利的时间和内存缩放,这使得它们不适合处理百万和十亿大小的集合。在这里,我们建议使用时间和内存效率高的聚类算法BitBIRCH来绕过这些问题。该方法使用类似于平衡迭代减少和聚类使用层次(BIRCH)算法的树结构,以确保O(N)时间尺度。BitBIRCH利用即时相似性(iSIM)形式来处理二进制指纹,允许使用谷本相似性,并降低内存需求。我们的测试表明,对于拥有150万个分子的库,BitBIRCH已经比Taylor-Butina聚类的标准实现快了1000倍。BitBIRCH在不影响集群质量的前提下提高了效率。我们探索了处理大集合的策略,我们使用并行/迭代BitBIRCH近似方法在5小时内将10亿个分子聚类。
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CiteScore
2.80
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0.00%
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