New deep learning model predicts antibody-antigen binding affinity from sequences

Researchers have developed DuaDeep-SeqAffinity, a novel deep learning framework designed to predict antibody-antigen binding affinity directly from amino acid sequences. This method bypasses the need for costly and scarce 3D structural data by processing antibody and antigen sequences through independent streams. The framework utilizes a dual-branch architecture with ESM-2 embeddings, Transformer, and CNN components, achieving strong performance on the AbRank benchmark and demonstrating preferential attention to critical binding regions. AI

IMPACT This model offers a scalable, structure-free tool for high-throughput antibody screening, potentially accelerating drug discovery and development.

RANK_REASON The cluster contains a research paper detailing a new deep learning model for a specific scientific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Abranková
• Aicha Boutorh
• arXiv
• CNN
• DuaDeep-SeqAffinity
• ESM-2
• Hugging Face
• Transformer++

• paper
• model release

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