Commit: BitBIRCH: efficient clustering of large molecular libraries.

We present BitBIRCH-Lean, a fast, memory-efficient implementation of the BitBIRCH algorithm, designed for high-throughput clustering of huge molecular libraries (up to billions of drug-like molecules) on modern scientific workstations. BitBIRCH-Lean considerably improves on the original BitBIRCH implementation by incorporating dynamic types and bit-packed fingerprints inside the clustering tree. Most operations in BitBIRCH-Lean are efficiently performed on compressed data, and optional C++ extension accelerates the bottleneck calculations, providing up to 2× speedup. Benchmark tests against GPU-accelerated methods highlight BitBIRCH-Lean as an efficient alternative for processing vast amounts of molecules. We further demonstrate the versatility of this new package by showcasing a parallel, multi-round variant of the BitBIRCH algorithm that exploits the gains in efficiency to cluster hundreds of millions of molecules in minutes, while preserving the same cluster distribution and properties. The code is freely available at: https://github.com/mqcomplab/bblean.