IGLM: Generating Human-Like Antibodies For Therapy? [Research]
Are we on the cusp of a revolution in therapeutic discovery? The answer is a resounding yes, thanks to the advent of innovative generative language models like iGLM, which are poised to redefine how we approach antibody engineering and drug development.
Research indicates that iGLM excels at generating full-length antibody sequences conditioned on chain type and source species. Beyond mere sequence generation, iGLM facilitates diversification within antibody loops, creating high-quality libraries exhibiting favorable biophysical properties akin to human antibodies. This is visually represented by an iGLM model overview for antibody sequence generation, as detailed in the original research paper. The implications of this are far-reaching, suggesting a future where therapeutic antibodies are more readily discoverable and developable.
| Category | Information |
|---|---|
| Model Name | iGLM (In Generative Language Model) |
| Developer | Gray Lab at Rosetta Commons |
| Training Data | 558 million natural antibodies |
| Function | Generates full antibody sequences, conditioned on species and chain type; enables sequence infilling for synthetic library design. |
| Key Benefit | Addresses developability issues in monoclonal antibodies, such as low stability, aggregation, and high immunogenicity. |
| Sister Project | Antiberty (another project by the Rosetta Commons' Gray Lab) |
| Application | Therapeutic antibody discovery and engineering. |
| Reference | Rosetta Commons |
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IgLM Infilling language modeling for antibody sequence design Cell
IgLM 条件式抗体生成模型 知乎
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