Understanding structure–activity relationships (SAR) of compounds is a central focus in chemical biology. Activity signatures are generated when a compound's activity is measured across many assays, and these signatures can be used to group compounds that have similar chemical–biological interactions. This approach does not need to consider chemical similarity and depends on large well-annotated chemical biology datasets.
In this article, the HTS data from 195 assays (both biochemical and cell-based assay data) developed at Novartis over a period of 10 years was used to derive the “high-throughput screening fingerprint” (HTS-FP) of compounds. The HTS-FP provides biological descriptors that can be analyzed by computational methods. In the article, HTS-FPs are used to develop target hypotheses and construct diverse subsets of bioactive compounds, and it is demonstrated that HTS-FPs are biologically relevant through examining gene ontology (GO) category enrichments within HTS-FP clusters.
To test the utility of HTS-FP in virtual screening, HTS-FPs were compared to a two-dimensional (2D) chemical similarity searching method, ECFP4 (extended connectivity atom environment with radius 4). Overall, for well-explored target classes like protein kinases for which there are many different types of chemotypes with many similar analogs, ECFP4 performed better in retrieving active compounds.
However, since HTS-FP doesn't depend on chemical structure, this method was far better at retrieving chemically diverse structures with similar biological activity. For example, when performing chemical similarity searches for the phosphodiesterase type 4C (PDE4C) inhibitor rolipram, compounds that were selected by chemical similarity contained at least one moiety in common with rolipram, while in the top 1% of the HTS-FP clusters many unrelated compounds were retrieved such as xanthanine derivatives. For these xanthanine compounds there are public data confirming PDE activity, with one compound reported as specific for PDE4C.