Work

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

Year: 2025

Type: article

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.

Source: Journal of Medicinal Chemistry

DOI: 10.1021/acs.jmedchem.5c01551

Authors: Nils Christiaan Bandomir, Tim Hörmann, Annette Kärcher, Astrid Kaiser, Daniel Merk, Pascal Heitel

Institutions: Goethe University Frankfurt, Ludwig-Maximilians-Universität München

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $4,500