Superimposed Coding of Count Fingerprints to Binary Fingerprints

Many cheminformatics workflows use Tanimoto similarity between binary fingerprints. When count fingerprints may be more appropriate, the benefit is often not large enough to justify replacing existing clustering tools, database search components, and other often highly optimized binary methods. This paper proposes a novel method based on random superimposed coding to convert count fingerprints to binary fingerprints such that the binary Tanimoto similarity between two binary fingerprints better approximates the multiset Tanimoto similarity between their original count form. In particular, the k-recall@k score for k-nearest neighbor search is consistently better than hash-based folding or RDKit's count simulation conversion across the four count fingerprint generators in the RDKit, if the number of features is relatively small compared to the binary fingerprint size. For example, with 2048-bit Morgan fingerprints of radius 3, the recall at for folded, count simulation, and superimposed coding are approximately 0.80, 0.86, and 0.94, respectively.