arXiv:2605.21600v1 Announce Type: new Abstract: Computational antibody CDR design methods condition on antigen structure to generate binding loops, yet existing architectures conflate two fundamentally distinct sub-problems: identifying which CDR positions will contact the antigen, and selecting amino acids at those positions. This conflation forces models to learn contact reasoning implicitly through uniform message passing, diluting antigen signal across all positions equally. We introduce ConTact, a contact-then-act architecture that explicitly decomposes CDR design into three cascaded stag
The paper introduces a novel architectural approach to computational antibody design, addressing a long-standing challenge in the field of AI for drug discovery.
This research provides a more efficient and targeted method for designing therapeutic antibodies, potentially accelerating drug development and reducing costs for pharmaceutical companies.
The explicit decomposition of CDR design into 'contact-then-act' steps will likely improve the accuracy and efficiency of AI models in antibody engineering, leading to more effective drug candidates.
- · Pharmaceutical companies
- · Biotech startups
- · AI in drug discovery platforms
- · Patients with immune-related diseases
- · Traditional antibody discovery methods
- · Companies reliant on less efficient computational approaches
Improved success rates in preclinical antibody drug development.
Increased investment and competition in the AI-driven therapeutic design sector.
Potentially faster and cheaper access to novel antibody therapies for a wider range of diseases.
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Read at arXiv cs.LG