arXiv:2605.28886v1 Announce Type: cross Abstract: Antibodies play a central role in the immune response by specifically recognizing and neutralizing antigens, and therapeutic antibodies have become major drugs for cancer and autoimmune diseases. However, their discovery still relies on extensive in vitro screening, and accurate computational modeling of antibody structures and antibody-antigen interactions can prioritize candidates, reduce experimental burden, and accelerate rational design. Despite recent advances in high-accuracy protein and complex prediction, a persistent performance gap r
Advances in AI, particularly Large Language Models (PLM-based) and other computational modeling techniques, are reaching a maturity where they can significantly impact drug discovery and therapeutic development.
Accurate computational modeling of antibody-antigen interactions can drastically reduce the cost and time of drug discovery for critical treatments like cancer and autoimmune diseases, accelerating the development of new therapies and improving public health outcomes.
The reliance on extensive in vitro screening for antibody discovery will decrease, shifting towards a more computationally driven, rapid, and targeted design process, making drug development more efficient and accessible.
- · Biopharmaceutical companies
- · AI/ML drug discovery platforms
- · Patients with cancer/autoimmune diseases
- · Healthcare sector
- · Traditional CROs focused solely on in vitro screening
- · R&D arms slow to adopt AI
Faster and cheaper development of new biologic drugs and therapeutic antibodies becomes possible.
Increased competition in the biopharmaceutical sector drives down drug costs and expands access to advanced treatments.
The application of similar AI modeling techniques could expand to other areas of protein engineering, material science, and bio-manufacturing, creating a new wave of engineered biological solutions.
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