docking的作用是计算哪种结合的分数更高

Great question. In the context of the image titled "Antibody Sequence Optimisation", the "Antibody-Antigen Docking" step is not primarily about scoring using embeddings, but rather about predicting the physical interaction and orientation between the antibody and antigen in 3D space. Here's a breakdown:

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🔍 Function of the Docking Step:

• Goal: To determine how well the predicted antibody structure fits or binds to the target antigen.
• Input: 3D structures of the antigen and antibody (obtained from previous structure modeling steps).
• Output: A docked complex — a structural model of the antibody bound to the antigen.

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💡 What's Happening in Docking:

1. Pose Generation:

   • Multiple possible binding orientations (poses) are generated.
   • This simulates how the antibody might approach and bind the antigen.

2. Scoring Functions (Energy-Based):

   • Each pose is scored using physical or statistical energy functions.
   • These scoring functions evaluate shape complementarity, electrostatic interactions, and binding energies.

3. Pose Ranking:

   • The best docking poses are ranked based on their binding energy or affinity prediction.
   • This ranking helps select the most likely native-like binding mode.

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🧠 Not Embedding-Based (Directly):

While embeddings from models (e.g., AlphaFold, ESM, OmegaFold) might help improve initial structure predictions or scoring in downstream tasks (e.g., ML-based affinity prediction), the docking step itself typically relies on physical modeling, not embeddings.

However, modern deep learning-based docking methods can incorporate learned features or embeddings — but that would typically be in a hybrid or post-processing step, not classical docking.

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✅ Summary:

• Docking simulates and scores physical interactions of antibody-antigen pairs.
• It's a structure-based method — using 3D models to find and rank the best fit.
• Affinity prediction comes after, and can use either:
  • Docking scores directly (e.g., binding energy), or
  • ML-based models that use structural or learned features.

Let me know if you want examples of specific docking tools or how ML is being fused into this pipeline!