Following insights from previous works , we analyzed our 3D pretraining backbone's learned representations using t-SNE method . The distribution of randomly selected 80,000 molecules from the validation set is colored by the molecular weights. As illustrated in Figure .c, the map displays the wide chemical space captured by this backbone. The highlighted molecules show that those with closer distances on the t-SNE map exhibit higher similarities in topological structures and functional groups, confirming the backbone's ability to identify complex molecular structures. It is critical for OOMs generation and property prediction.