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A common strategy for identifying molecules likely to possess a desired biological activity is to search large databases of compounds for high structural similarity to a query molecule that demonstrates this activity, under the assumption that structural similarity is predictive of similar biological activity. However, efforts to systematically benchmark the diverse array of available molecular fingerprints and similarity coefficients have been limited by a lack of large-scale datasets that reflect biological similarities of compounds. To elucidate the relative performance of these alternatives, we systematically benchmarked 11 different molecular fingerprint encodings, each combined with 13 different similarity coefficients, using a large set of chemical-genetic interaction data from the yeast Saccharomyces cerevisiae as a systematic proxy for biological activity. We found that the performance of different molecular fingerprints and similarity coefficients varied substantially and that the all-shortest path fingerprints paired with the Braun-Blanquet similarity coefficient provided superior performance that was robust across several compound collections. We further proposed a machine learning pipeline based on support vector machines that offered a fivefold improvement relative to the best unsupervised approach. Our results generally suggest that using high-dimensional chemical-genetic data as a basis for refining molecular fingerprints can be a powerful approach for improving prediction of biological functions from chemical structures.

Toll-like receptors (TLRs) play a critical role in innate immunity, but activation of TLR signaling pathways is also associated with many harmful inflammatory diseases. Identification of novel anti-inflammatory molecules targeting TLR signaling pathways is central to the development of new treatment approaches for acute and chronic inflammation. We performed high-throughput screening from crude marine sponge extracts on TLR5 signaling and identified girolline. We demonstrated that girolline inhibits signaling through both MyD88-dependent and -independent TLRs (i.e., TLR2, 3, 4, 5, and 7) and reduces cytokine (IL-6 and IL-8) production in human peripheral blood mononuclear cells and macrophages. Using a chemical genomics approach, we identified Elongation Factor 2 as the molecular target of girolline, which inhibits protein synthesis at the elongation step. Together these data identify the sponge natural product girolline as a potential anti-inflammatory agent acting through inhibition of protein synthesis.