Natural products (NPs) are progressively recognized as invaluable source of pharmacological tools and lead structures. To enable NP-inspired retinoid X receptor (RXR) modulator design, three novel RXR-targeting NPs were computationally identified. Among them, valerenic acid was found to be selective for RXRβ, rendering it a unique pharmacological tool compound. The NPs then served as templates for automated, ligand-based de novo design of innovative, easily accessible mimetics that inherited the biological activities of their natural templates.

The lack of potent subtype-selective modulators of retinoid X receptors (RXRs) has hindered their full exploitation as promising drug targets. Using computational similarity searching, target prediction and automated design, we identified novel RXR ligands exhibiting innovative molecular frameworks, pronounced receptor-subtype preference and suitable properties for hit-to-lead expansion.

Deep learning neural networks, constructed for the prediction of chemical binding at 79 pharmacologically important human biological targets, show extremely high performance on test data (accuracy 92.2 ± 4.2%, MCC 0.814 ± 0.093, ROC-AUC 0.96 ± 0.04).