Classical graph-based methods excel in analysing chemical toxicity but struggle with repetitive structures such as those in metal-oxide nanocompounds. To solve this problem, GraphFingerprints were designed as a graph embedding useful for almost constant substructures. This paper presents several applications in which GraphFingerprints have been applied to metal-oxide nanocompound analysis and observes its usefulness through some practical experiments. Specifically, we present toxicity analysis, sub-graph detection and the computation of GraphFingerprints by a generative algorithm. Results demonstrate that combining GraphFingerprints with traditional graph techniques enhances prediction accuracy.