Differential Multimolecule Fingerprint for Similarity SearchMaking Use of Active and Inactive Compound Sets in Virtual Screening

In conventional fingerprint
methods, the similarity between two
molecules is calculated using the Tanimoto index as a numerical criterion.
Thus, the query molecules in virtual screening should be most representative
of the wanted compound class at hand. In the concept introduced here,
all available active molecules form a multimolecule fingerprint in
which the appearing features are weighted according to their respective
frequency. The features of inactive molecules are treated likewise
and the resulting values are subtracted from those of the active ones.
The obtained differential multimolecule fingerprint (DMMFP) is thus
specific for the respective class of compounds. To account for the
noninteger representation within this fingerprint, a modified Sørensen–Dice
coefficient is used to compute the similarity. Potentially active
molecules yield positive scores, whereas presumably inactive ones
are denoted by negative values. The concept was applied to Angiotensin-converting
enzyme (ACE) inhibitors, β2-adrenoceptor ligands, leukotriene
A4 hydrolase inhibitors, dopamine D3 antagonists, and cytochrome CYP2C9
substrates, for which experimental binding affinities are known and
was tested against decoys from DUD-E and a further background database
consisting of molecules from the dark chemical matter, which comprises
compounds that appear as frequent hitters across multiple assays.
Using the 166 publicly available keys of the MACCS fingerprint and
the larger PubChem fingerprint, actives were recovered with very high
sensitivity. Furthermore, three marketed ACE inhibitors as well as
the carbonic anhydrase II inhibitor dorzolamide were detected in the
dark chemical matter data set. For comparison, the DMMFP was also
used with a Bayesian classifier, for which the specificity (correctly
classified inactives) and likewise the accuracy was superior. Conversely,
the similarity score produced by the Sørensen–Dice coefficient
showed its potential for the early recognition of (potentially) active
molecules.