AI BriefingLG AI Research 290
Key point
LG AI Research developed a new attention mechanism-based AI model that improves HLA-peptide binding prediction performance.
Details
Existing 1st-generation anticancer drugs (cytotoxic) and 2nd-generation anticancer drugs (targeted therapies) have limitations such as attacks on normal cells or side effects caused by mutant genes. As an alternative to this, 3rd-generation immuno-oncology drugs, which stimulate the immune system to selectively attack only cancer cells, are drawing attention.
The key to developing immuno-oncology drugs is understanding the relationship between HLA (Human Leukocyte Antigen) and Neoepitope. Peptides of 8-10 amino acids bound to HLA Class I molecules are expressed on the cell surface and play a decisive role in enabling T cells to identify cancer cells.
LG AI Research's Material Intelligence (MI) Lab built an HLA-peptide binding prediction model for the development of personalized cancer vaccines. Recently, self-supervised models based on ESM or BERT have emerged, but research on how to efficiently utilize attention mechanisms in situations with relatively scarce data is still lacking.
To address this, LG AI Research proposed a Conditional Attention Mechanism that focuses on HLA structure by utilizing peptide information. This model showed superior performance compared to existing complex Concatenated or Cross attention methods, and to overcome the limitations of fragmented existing models, LG AI Research integrated source code and datasets to build a fair comparison environment.
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