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Here we present Graphinity, an equivariant graph neural network architecture built directly from antibody\u2013antigen structures that achieves test Pearson\u2019s correlations of up to 0.87 on experimental change in binding affinity (\u0394\u0394\n                    <jats:italic>G<\/jats:italic>\n                    ) prediction. However, our model, like previous methods, appears to be overtraining on the few hundred experimental data points available and performance is not robust to train\u2013test cut-offs. To investigate the amount and type of data required to generalizably predict \u0394\u0394\n                    <jats:italic>G<\/jats:italic>\n                    , we built synthetic datasets of nearly 1 million FoldX-generated and &gt;20,000 Rosetta Flex ddG-generated \u0394\u0394\n                    <jats:italic>G<\/jats:italic>\n                    values. Our results indicate that there are currently insufficient experimental data to accurately and robustly predict \u0394\u0394\n                    <jats:italic>G<\/jats:italic>\n                    , with orders of magnitude more likely needed. Dataset size is not the only consideration; diversity is also an important factor for model predictiveness. These findings provide a lower bound on data requirements to inform future method development and data collection efforts.\n                  <\/jats:p>","DOI":"10.1038\/s43588-025-00823-8","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T05:02:34Z","timestamp":1751950954000},"page":"635-647","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Investigating the volume and diversity of data needed for generalizable antibody\u2013antigen \u0394\u0394G prediction"],"prefix":"10.1038","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3023-2588","authenticated-orcid":false,"given":"Alissa M.","family":"Hummer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Constantin","family":"Schneider","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lewis","family":"Chinery","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1388-2252","authenticated-orcid":false,"given":"Charlotte M.","family":"Deane","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,8]]},"reference":[{"key":"823_CR1","doi-asserted-by":"publisher","first-page":"2146629","DOI":"10.1080\/19420862.2022.2146629","volume":"14","author":"EK Makowski","year":"2022","unstructured":"Makowski, E. 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