𝗧𝗟𝗗𝗥: After you predict a complex, run DockQ. pLDDT isn’t an interface score, so use DockQ before you trust a pose.   
  
AlphaFold is great, but high pLDDT is not an interface score. It says the local backbone looks confident. It does not tell you if two chains are actually making the right contacts. For that you need something else.   
  
Picture this: you dock an antibody to a target. The model looks clean, pLDDT is green, figures are pretty. But CDRH3 (antibody binding part) is skating past the epitope and the paratope residues. Wet lab fails and you blame docking. Now sort your poses by DockQ. A different model jumps to the top. The backbone shifts a little, the loops clasp the right patch, and a quick contact map shows the expected residues actually meet. That is the one you should take forward.  
  
DockQ is the interface sanity check. It compresses three signals into one 0-1 score you can rank on:  
 • Fnat: are the right residues touching  
 • iRMSD: how much the interface backbone moved  
 • LRMSD: how far the ligand shifted after aligning on the receptor  
  
𝗖𝗵𝗲𝗰𝗸 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗯𝗹𝗼𝗴: [Here](https://www.vici.bio/blog/what-is-the-dockq-score-and-when-to-trust-it)  
  
𝗥𝘂𝗻 𝘆𝗼𝘂𝗿 𝗳𝗶𝗿𝘀𝘁 𝗗𝗼𝗰𝗸𝗤 𝗷𝗼𝗯 𝗼𝗻 [Vici.bio](https://www.linkedin.com/company/vicibio/): [Here](https://www.vici.bio/platform)  
   
𝗧𝗲𝗹𝗹 𝘂𝘀 𝘄𝗵𝗮𝘁'𝘀 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗼𝗿 𝗽𝗮𝗿𝘁𝗻𝗲𝗿: [info@vici.bio](mailto:info@vici.bio)  
  
𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀:  
[https://github.com/wallnerlab/DockQ](https://github.com/wallnerlab/DockQ)

[https://doi.org/10.1093/bioinformatics/btae586](https://doi.org/10.1093/bioinformatics/btae586)

𝗜𝗳 𝘆𝗼𝘂 𝘂𝘀𝗲 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗼𝗿 𝗱𝗼𝗰𝗸𝗶𝗻𝗴, 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗳𝗼𝗿 𝘆𝗼𝘂. 😁