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

## TL;DR

A chemical language model was used to design new Liver X Receptor modulators with promising activity in treating metabolic disorders.

## Contribution

The use of a chemical language model to merge structural features and transfer SAR knowledge for novel LXR modulator design.

## Key findings

- Generated modulators showed diverse activity profiles and selectivity.
- An inverse LXR agonist demonstrated promising lipolytic activity in an in vitro MASLD model.

## Abstract

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.