Advancements in antibody-antigen complex prediction using AI, particularly through models like AlphaFold 3 and RFDiffusion, have enhanced drug design, offering high target specificity and the ability to tackle previously undruggable targets. However, challenges include structural complexities, limitations in modeling performance, and the need for rigorous benchmarking of AI tools. By improving data collection and confidence metrics, researchers aim to streamline antibody therapeutics discovery, turning the promise of AI into practical solutions for unmet medical needs.
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