Missense variants, that is, single amino acid substitutions in proteins, are often difficult to assess. Our machine learning workflow uses protein structure-based graph embeddings to predict the pathogenicity of such variants. In doing so, the structural information enhances existing approaches like the CADD score and provides new insights for genomic medical diagnostics.