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Abstract The nine human NR3 nuclear receptors translate steroid hormone signals in transcriptomic responses and operate multiple highly important processes ranging from development over reproductive tissue function to inflammatory and metabolic homeostasis. Although several NR3 ligands such as glucocorticoids are invaluable drugs, this family is only partially explored, for example, in autoimmune diseases and neurodegeneration, but may hold therapeutic potential in new areas. Here we report a chemogenomics (CG) library to reveal elusive effects of NR3 receptor modulation in phenotypic settings. 34 highly annotated and chemically diverse ligands covering all NR3 receptors were selected considering complementary modes of action and activity, selectivity and lack of toxicity. Endoplasmic reticulum stress resolving effects of N3 CG subsets in proof-of-concept application validate suitability of the set to connect phenotypic outcomes with targets and to explore NR3 receptors from a translational perspective.

Abstract Generative deep learning models enable data-driven de novo design of molecules with tailored features. Chemical language models (CLM) trained on string representations of molecules such as SMILES have been successfully employed to design new chemical entities with experimentally confirmed activity on intended targets. Here, we probe the application of CLM to generate multi-target ligands for designed polypharmacology. We capitalize on the ability of CLM to learn from small fine-tuning sets of molecules and successfully bias the model towards designing drug-like molecules with similarity to known ligands of target pairs of interest. Designs obtained from CLM after pooled fine-tuning are predicted active on both proteins of interest and comprise pharmacophore elements of ligands for both targets in one molecule. Synthesis and testing of twelve computationally favored CLM designs for six target pairs reveals modulation of at least one intended protein by all selected designs with up to double-digit nanomolar potency and confirms seven compounds as designed dual ligands. These results corroborate CLM for multi-target de novo design as source of innovation in drug discovery.

Abstract Nuclear receptors (NRs) regulate transcription in response to ligand binding and NR modulation allows pharmacological control of gene expression. Although some NRs are relevant as drug targets, the NR1 family, which comprises 19 NRs binding to hormones, vitamins, and lipid metabolites, has only been partially explored from a translational perspective. To enable systematic target identification and validation for this protein family in phenotypic settings, we present an NR1 chemogenomic (CG) compound set optimized for complementary activity/selectivity profiles and chemical diversity. Based on broad profiling of candidates for specificity, toxicity, and off-target liabilities, sixty-nine comprehensively annotated NR1 agonists, antagonists and inverse agonists covering all members of the NR1 family and meeting potency and selectivity standards are included in the final NR1 CG set. Proof-of-concept application of this set reveals effects of NR1 members in autophagy, neuroinflammation and cancer cell death, and confirms the suitability of the set for target identification and validation.