Do molecular fingerprints identify diverse active drugs in large-scale virtual screening? (no)

Abstract

Computational approaches for small-molecule drug discovery now regularly scale to consideration of libraries containing billions of candidate small molecules. One promising approach to increased speed in evaluating billion-molecule libraries is to develop succinct representations of each molecule that enable rapid identification of molecules with similar properties. Molecular fingerprints are thought to provide a mechanism for producing such representations. Here, we explore the utility of commonly-used fingerprints in the context of predicting similar molecular activity. We show that fingerprint similarity provides little discriminative power between active and inactive molecules for a target protein based on a known active - while they may sometimes provide some enrichment for active molecules in a drug screen, a screened dataset will still be dominated by inactive molecules. We also demonstrate that high-similarity actives appear to share a scaffold with the query active, meaning that they could more easily be identified by structural enumeration. Furthermore, even when limited to only active molecules, fingerprint similarity values do not correlate with compound potency. In sum, these results highlight the need for a new wave of molecular representations that will improve the capacity to detect biologically active molecules based on similarity to other such molecules.