Scaffold Fusion and SAR Transfer with a Chemical Language Model Generates Novel Liver X Receptor Modulators

Liver X receptors
(LXRs) are promising targets for metabolic disorders
including atherosclerosis and metabolic dysfunction-associated steatotic
liver disease (MASLD). In this study, we employed a chemical language
model (CLM) for LXR modulator design in an explorative fashion and
observed that structural features from different LXR modulator templates
were merged, and structure–activity relationship (SAR) knowledge
was transferred. The generated computational designs demonstrated
LXR modulation with diverse activity profiles and selective modulator
properties, including a promising lipolytic activity of an inverse
LXR agonist in an in vitro MASLD model that warrants its further development
to improve ADME properties.