Now published: We trained a chemical language model that works with both small molecules and peptides. Ideal for making predictions on peptides with non-standard amino acids or other chemical modifications.
https://pubs.acs.org/doi/full/10.1021/acs.jcim.4c01441

Despite the title which suggests this is particularly for predicting membrane diffusion, the model is general and can be fine-tuned to any downstream application you may be interested in.

If you don't have access to the journal pub, the bioRxiv version is here:

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Despite the title which suggests this is particularly for predicting membrane diffusion, the model is general and can be fine-tuned to any downstream application you may be interested in.

If you don't have access to the journal pub, the bioRxiv version is here: https://www.biorxiv.org/content/10.1101/2024.08.09.607221v2.abstract

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Any chemical modification, and roughly 100 amino acids. Our context window is 768 tokens, and most amino acids require about 7 tokens to be represented, but details depend on the size of the amino acid. (The larger amino acids need more tokens.)

What is the limitation on the length of the peptide and can it work for 'any' chemical modification or just methylation / pegylation?

Also - can 'targeted' membrane diffusion be predicted for a specific modification or is it just 'general diffusion' ?