We use all unique circular environments, similar to ECFPs, replacing the bit vector with a float value vector that encodes molecular fragment molarity, thus capturing both structural features and stoichiometry. Chemical similarity comparisons for drug formulations do not take into account stoichiometry. Our float-vector fingerprints (FVFPs), specifically designed for mixtures (Fig 1), emphasize chemical fragments present multiple times (i.e., with higher stoichiometry), which become the dominant contributor to the value of the similarity coefficient.<br/><br/>For example, JANUMET (with 50 mg sitagliptin), PRANDIMET (with 1 mg repaglinide), METAGLIP (with 2.5 mg glipizide) and AVANDAMET (with 2 mg rosiglitazone), all contain 500 mg metformin (dominant). This implementation is suitable for studying mechanistic trends and uses of mixture components and dosages. However, centrally located atoms/fragments are captured more often in both ECFPs and FVFPs, whereas peripheral ones are considered less.<br/><br/>We therefore introduced molecular-weight (MW) correction, forcing the molarity per atom across all fragments to be equal to that molecule's molarity, thus MW-corrected fingerprints (MFPs). Fig 2 shows the Tanimoto similarity values of MFPs and FVFPs for 2 drug mixtures containing 10 mg Phenylephrine. Heavier atoms have a bigger contribution to MFPs, which are more likely to be sensitive to small dose changes. While from the perspective of single chemical comparisons not all fragments in Fig.2 share high Tanimoto similarity, these two mixtures are similar according to our float vector methods. These methods can serve to compare, track, archive and retrieve chemical mixtures directly in database operations. For example, different pharmaceutical formulations can be clustered to identify those have the same therapeutic applications, opening up the potential for computer-aided drug repurposing.

Chemical mixture evaluation using molecular-weight corrected fingerprints

256th National Meeting (2018)

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