A generative foundation model for antibody sequence understanding

Barton, J.; Gaspariunas, A.; Yadin, D. A.; Dias, J.; Nice, F. L.; Minns, D. H.; Snudden, O.; Povall, C.; Valle Tomas, S.; Dobson, H.; Farmery, J. H.; Leem, J.; Galson, J. D.

doi:10.1101/2024.05.22.594943

bioRxiv · 10.1101/2024.05.22.594943

A generative foundation model for antibody sequence understanding

Barton, J.·Gaspariunas, A.·Yadin, D. A.·Dias, J.·Nice, F. L.·Minns, D. H.·Snudden, O.·Povall, C.·Valle Tomas, S.·Dobson, H.·Farmery, J. H.·Leem, J.·Galson, J. D.

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Abstract

Here we introduce FAbCon, a generative antibody-specific language model comprising 2.4 billion parameters. A commonly accepted wisdom in developing large language models is that increasing model scale will translate to higher performance on downstream tasks. Starting from a 144-million parameter setup, we show that progressively larger models achieve greater accuracy in predicting antigen binding and can also be used to design new antibodies with good predicted developability potential. FAbCon is available on huggingface.co/alchemab.

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Barton, J., Gaspariunas, A., Yadin, D. A., Dias, J., Nice, F. L., Minns, D. H., Snudden, O., Povall, C., Valle Tomas, S., Dobson, H., Farmery, J. H., Leem, J., Galson, J. D.. 2024-05-26. A generative foundation model for antibody sequence understanding. https://doi.org/10.1101/2024.05.22.594943

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