Abstract The exploration and mapping of chemical space remain a central challenge in modern drug discovery. Traditional compound libraries and databases cover only a minute fraction of this space, limiting the discovery of novel, bioactive, and patentable chemotypes. Here, we present a unique dataset containing approximately 110 M molecular structures of potential NLRP3 inhibitors enabled by the LEGION ( Latent Enumeration, Generation, Integration, Optimization, and Navigation ) workflow, which integrates generative AI, AI-guided screening within the Chemistry42 platform and auxiliary cheminformatics tools to enable large-scale exploration of chemical space around specific drug targets. Using the structural data of NLRP3 co-crystals, a clinically relevant target, LEGION combined ligand- and structure-based design strategies, in-house algorithms for 3D pharmacophore-aware scaffold extraction, and distinct library enumeration methods to identify over 34,000 unique scaffolds, which can be multiplied into a dataset of 123B molecular structures within the provided code. The resulting dataset of unprecedented size proved effective for scaffold hopping, chemical space navigation, and supporting intellectual property applications by generating structurally diverse and synthetically accessible structures.
Zagribelnyy et al. (Tue,) studied this question.