Domain adaptable language modeling of chemical compounds identifies potent pathoblockers for Pseudomonas aeruginosa

Abstract Computational techniques for predicting molecular properties are emerging as key components for streamlining drug development, optimizing time and financial investments. Here, we introduce ChemLM, a transformer language model for this task. ChemLM leverages self-supervised domain adaptation...

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Bibliographic Details

Published in:	Communications Chemistry
Main Authors:	Georgios Kallergis, Ehsannedin Asgari, Martin Empting, Anna K. H. Hirsch, Frank Klawonn, Alice C. McHardy
Format:	Article
Language:	English
Published:	Nature Portfolio 2025-04-01
Online Access:	https://doi.org/10.1038/s42004-025-01484-4