What does AlphaFold3 learn about antigen and nanobody docking, and what remains unsolved?

Antibody therapeutic development is a major focus in healthcare. To accelerate drug development, significant efforts have been directed towards the in silico design and screening of antibodies for which high modeling accuracy is necessary. To probe AlphaFold3's (AF3) capabilities and limitations, we tested AF3's ability to capture the fine details and interplay between antibody structure prediction and antigen docking accuracy. With one seed, AF3 achieves an 11.0% and 11.4% high-accuracy docking success rate for antibodies and nanobodies, respectively, and a median unbound CDR H3 RMSD accuracy of 2.73 Å and 2.30 Å. CDR H3 accuracy boosts complex prediction accuracy, with antigen context improving CDR H3 accuracy, particularly for loops longer than 15 residues. Combining I-pLDDT with $\text{Δ}{G}_B$ improves discriminative power for correctly docked complexes. However, AF3's 60% failure rate for antibody and nanobody docking (with single seed sampling) demonstrates necessary refinement to improve antibody design endeavors.