Bioisosteric similarity of molecules based on structural alignment and observed chemical replacements in drugs.

The algorithmic concept used to assess the evolutionary relationship between protein sequences was adopted to the comparison of drug-like compounds. For this purpose, we have developed a method that uses the SMILES representation of the molecules to perform the corresponding pairwise alignment. The necessary exchange matrix was generated in an automated procedure that reflects the frequencies of chemical replacements in pharmaceutical substances. From the resulting alignment, the relationship between two molecules is computed as so-called bioisosteric similarity. This measure was used to perform virtual screening in several publicly available substance databases. We observed that databases containing drug-like compounds throughout showed higher bioisosteric similarities to the query compound than our reference set of confirmed nondrugs. Likewise, most actual drugs within a class show a higher bioisosteric similarity than the large background of other substances. The compounds obtained as highest ranking hits from the lead-like subset of the ZINC library showed distinct differences in comparison with corresponding results from a fingerprint-based similarity search, as well as the FTrees method. In particular the kind of chemical replacements as well as the conservation of substructures strongly reflect the underlying bioisosteric exchanges. Moreover, the bioisosteric similarity was used to assess the chemical diversity of the utilized drug classes and to compute the "average" molecule within the respective class.