Molecular similarity is one of the most central concepts in chemoinformatics. Typical measures of molecular similarity (such as the Tanimoto coefficient of binary fingerprints) are used for tasks such as similarity search, distinguishing similar molecules from dissimilar (e.g. identifying actives in a virtual screen), measuring the diversity of a dataset or selecting a diverse subset. While the measurement of R group similarity is conceptually the same as for whole molecules, in practice existing methods for measuring molecular similarity perform poorly. Improved methods to measure R group similarity are of particular importance in the context of a medicinal chemistry project. These often proceed by changing one R group at a time, advancing through matched pairs. Given an appropriate measure of R group similarity, it should be possible to suggest relevant modifications or identify gaps in the project data that should be filled. In a computational context, R group enumeration could be used to generate relevant candidate molecules for virtual screening or purchase. In medicinal chemistry, the term bioisosteric replacement refers to a substitution that retains broadly similar biological properties. However, there is a need to go beyond the concept of bioisoteres/non-bioisosteres to handle levels of similarity; for example, chloro is regarded as more similar to fluoro than to amino. Clearly, in this context, the extent to which two R groups are similar is not just (or not even) a question of shared substructures. Previous approaches to this problem include the use of R group descriptors by Holliday et al which maps atom-based descriptor values onto a vector by distance from the attachment point, and the use of reduced graphs to encode bioisosteric equivalences by Birchall et al. We propose to use co-occurrence in medicinal chemistry project data to derive a measure of R group similarity. We will use two distinct sources for these data, both in the public domain. The first is the ChEMBL database, which contains assay data extracted from a range of medicinal chemistry journals. The other source is the US patent literature, which provides text and ChemDraw sketches from which a large amount of medicinal chemistry data can be extracted. While large-scale mining of medicinal chemistry data has previously been used to detect bioisosteres, to our knowledge this is the first time it has been used to develop a method to measure R group similarity.

Measuring R group similarity using medicinal chemistry data

2019 Fall National Meeting

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