Hashing algorithms and data structures for rapid searches of fingerprint vectors.

In many large chemoinformatics database systems, molecules are represented by long binary fingerprint vectors whose components record the presence or absence of particular functional groups or combinatorial features. To speed up database searches, we propose to add to each fingerprint a short signature integer vector of length M. For a given fingerprint, the i component of the signature vector counts the number of 1-bits in the fingerprint that fall on components congruent to i modulo M.

An intersection inequality sharper than the tanimoto triangle inequality for efficiently searching large databases.

Bounds on distances or similarity measures can be useful to help search large databases efficiently. Here we consider the case of large databases of small molecules represented by molecular fingerprint vectors with the Tanimoto similarity measure. We derive a new intersection inequality which provides a bound on the Tanimoto similarity between two fingerprint vectors and show that this bound is considerably sharper than the bound associated with the triangle inequality of the Tanimoto distance.

Speeding up chemical database searches using a proximity filter based on the logical exclusive or.

In many large chemoinformatics database systems, molecules are represented by long binary fingerprint vectors whose components record the presence or absence in the molecular graphs of particular functional groups or combinatorial features, such as labeled paths or labeled trees. To speed up database searches, we propose to store with each fingerprint a small header vector containing primarily the result of applying the logical exclusive OR (XOR) operator to the fingerprint vector after modulo wrapping to a smaller number of bits, such as 128 bits. From the XOR headers of two molecules, tight bounds on the intersection and union of their fingerprint vectors can be rapidly obtained, yielding tight bounds on derived similarity measures, such as the Tanimoto measure.

Lossless compression of chemical fingerprints using integer entropy codes improves storage and retrieval.

Many modern chemoinformatics systems for small molecules rely on large fingerprint vector representations, where the components of the vector record the presence or number of occurrences in the molecular graphs of particular combinatorial features, such as labeled paths or labeled trees. These large fingerprint vectors are often compressed to much shorter fingerprint vectors using a lossy compression scheme based on a simple modulo procedure. Here, we combine statistical models of fingerprints with integer entropy codes, such as Golomb and Elias codes, to encode the indices or the run lengths of the fingerprints.