Abstract
In response to the pressing global challenge of antibiotic resistance, time efficient design and synthesis of novel antibiotics are of immense need. Polycyclic polyprenylated acylphloroglucinols (PPAP) were previously reported to effectively combat a range of gram-positive bacteria. Although the exact mode of action is still not clear, we conceptualized a late-stage divergent synthesis approach to expand our natural product-based PPAP library by 30 additional entities to perform SAR studies against methicillin-resistant Staphylococcus aureus (MRSA). Although at this point only data from cellular assays are available and understanding of molecular drug-target interactions are lacking, the experimental data were used to generate 3D-QSAR models via an artificial intelligence training and to identify a common pharmacophore model. The experimentally validated QSAR model enabled the estimation of anti-MRSA activities of a virtual compound library consisting of more than 100,000 in-silico generated B PPAPs, out of which the 20 most promising candidates were synthesized. These novel PPAPs revealed significantly improved cellular activities against MRSA with growth inhibition down to concentrations less than 1 μm.
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