Chemical matter with often-discarded moieties entails opportunities for drug discovery. Relying on orthogonal ligand-centric machine learning methods, targets were consensually identified as potential counterparts for the fragment-like natural product β-lapachone. Resorting to a comprehensive range of biophysical and biochemical assays, the natural product was validated as a potent, ligand efficient, allosteric and reversible modulator of 5-lipoxygenase (5-LO). Moreover, we provide a rationale for 5-LO-inhibiting chemotypes inspired in the β-lapachone scaffold through a focused analogue library. This work demonstrates the power of artificial intelligence technologies to deconvolute complex phenotypic readouts of clinically relevant chemical matter, leverage natural product-based drug discovery, as an alternative and/or complement to chemoproteomics and as a viable approach for systems pharmacology studies.