Fig. 3
From: Evaluating the generalizability of graph neural networks for predicting collision cross section
Scatterplots of the predictions for each model when training on one database and evaluating on another one. The bottom plots show the evaluation on CCSBase when training on METLIN-CCS. When training on CCSBase and evaluating on METLIN-CCS (upper row) the performance of all models significantly drops. For instance, the R2 goes down to 0.36, 0.8, and 0.84 for GraphCCS, SigmaCCS, and Mol2CCS, respectively. However, performance drops less dramatically when training on METLIN-CCS and evaluating on CCSBase, since the models have been trained on several times more data points. Despite the larger training data, the differences in their chemical space can explain why all models exhibit RMSEs three times larger than when they are trained and evaluated on the same database