BioCompoundML: A General Biofuel Property Screening Tool for Biological Molecules Using Random Forest Classifiers
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The chemical diversity of nature has tremendous potential for the discovery of molecular probes and medicinal agents. However, sensitivity of HTS assays to interfering components of crude extracts derived from plants, and macro- and microorganisms has curtailed their use in lead discovery. Here, we describe a process for leveraging the concentration-response curves obtained from quantitative HTS to improve the initial selection of actives from a library of partially fractionated natural product extracts derived from marine actinomycetes and fungi. By using pharmacological activity, the first-pass CRC paradigm improves the probability that labor-intensive subsequent steps of reculturing, extraction, and bioassay-guided isolation of active component(s) target the most promising strains and growth conditions. We illustrate how this process identified a family of fungal metabolites as potent inhibitors of firefly luciferase, subsequently resolved in molecular detail by X-ray crystallography.
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