Computational Antibody Papers

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language models
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2025
TitleKey points

  • 2025-02-20

    Few-shot in-context learning with large language models for antibody characterization

    • language models
    • Demonstration that general purpose language models - like GPT3.5 - can reason about antibody - engineering tasks.
    • Authors explore the topic of in context learning - e.g. few shot learning where several examples are given and on the basis of that the model needs to provide a prediction for a new case.
    • They tested an array of general purpose models, such as GPTs, LLamas, Mistrals etc.
    • They tested on three antibody tasks - mouse/human discrimination, specificity prediction (from ngs) and isotype identification. In theory not that difficult tasks, but remember we are dealing witha general purpose language model.
    • They literally prompt the model with examples on, say mouse antibodies, human antibodies and provide a next one to predict.
    • They find that the predictions are not bad, especially in few shot scenario (16 examples or so).
    • In one test it even achieved accuracy on par with AntiBERTy.

  • 2025-02-17

    DyAb: sequence-based antibody design and property prediction in a low-data regime

    • binding prediction
    • language models
    • Method to employ low-N data for biologic engineering.
    • Assuming we have a dataset of ~100 affinity data points, we can choose (100 choose 2) pairs where we know which one has a larger readout than the other (e.g. stronger affinity) giving combinatorially larger amount of data points to train on.
    • The architecture used is CNN on top of a language model.
    • Benchmarked on three internal campaigns, Il6, EGFR and an undisclosed target.