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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
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| Series: | Communications Chemistry |
| Online Access: | https://doi.org/10.1038/s42004-025-01484-4 |
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