Rethinking Molecular Similarity: Comparing Compounds on the Basis of Biological Activity

Since the advent of high-throughput screening (HTS),
there has
been an urgent need for methods that facilitate the interrogation
of large-scale chemical biology data to build a mode of action (MoA)
hypothesis. This can be done either prior to the HTS by subset design
of compounds with known MoA or post HTS by data annotation and mining.
To enable this process, we developed a tool that compares compounds
solely on the basis of their bioactivity: the chemical biological
descriptor “high-throughput screening fingerprint” (HTS-FP).
In the current embodiment, data are aggregated from 195 biochemical
and cell-based assays developed at Novartis and can be used to identify
bioactivity relationships among the in-house collection comprising
∼1.5 million compounds. We demonstrate the value of the HTS-FP
for virtual screening and in particular scaffold hopping. HTS-FP outperforms
state of the art methods in several aspects, retrieving bioactive
compounds with remarkable chemical dissimilarity to a probe structure.
We also apply HTS-FP for the design of screening subsets in HTS. Using
retrospective data, we show that a biodiverse selection of plates
performs significantly better than a chemically diverse selection
of plates, both in terms of number of hits and diversity of chemotypes
retrieved. This is also true in the case of hit expansion predictions
using HTS-FP similarity. Sets of compounds clustered with HTS-FP are
biologically meaningful, in the sense that these clusters enrich for
genes and gene ontology (GO) terms, showing that compounds that are
bioactively similar also tend to target proteins that operate together
in the cell. HTS-FP are valuable not only because of their predictive
power but mainly because they relate compounds solely on the basis
of bioactivity, harnessing the accumulated knowledge of a high-throughput
screening facility toward the understanding of how compounds interact
with the proteome.