BitBIRCH: efficient clustering of large molecular libraries
Kenneth López Pérez, Vicky Jung, Lexin Chen, Kate Huddleston, Ramón Alain Miranda‐Quintana
Abstract
) 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.
Topics & Concepts
Cluster analysisComputer scienceInformation retrievalComputational biologyBiologyArtificial intelligenceComputational Drug Discovery MethodsAnalytical Chemistry and ChromatographyProtein Structure and Dynamics