Huddleston, K. | bioRxiv Science

bioRxiv2025

BitBIRCH Clustering Refinement Strategies

Chemical libraries are becoming not only increasingly bigger, but they are doing so at an accelerated pace. Keeping up with this explosion in chemical data demands more than just hardware upgrades, we need dramatically more efficient algorithms as well. We have been working in this direction, with the introduction of the iSIM framework, which uses n-ary similarity to speed up the processing of very large sets. Recently, we showed how to use this technique to cluster billions of molecules with unprecedented efficiency through the BitBIRCH algorithm. In this Application Note we present a package fully-dedicated to expanding on the BitBIRCH method, including multiple options that give the user appreciable control over the tree structure, while dramatically improving the quality of the final partitions. Remarkably, this is achieved without compromising the efficiency of the original method. We also present new post-processing tools that help dissect the clustering information, as well as ample examples showcasing the new functionalities. BitBIRCH is publicly available at: https://github.com/mqcomplab/bitbirch.

bioinformatics↗

bioRxiv2024

Efficient clustering of large molecular libraries

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 > 1,000 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.

bioinformatics↗