## 3 Results
### Molecular Generation Based on Transfer Learning  
(1) Effect of Data Augmentation  
Due to the relative lack of PROTAC Linker data, data augmentation, a technique that expands the training dataset by generating additional samples, was used to provide more data for ProLinker-Generator. In molecular science, data augmentation is typically achieved through transformations of the SMILES representation of molecules, such as atomic rearrangements, isomer introductions, or substructure replacements [49]. These transformations aim to mitigate the challenges posed by the high dimensionality and complexity of chemical space, thereby enabling models to better capture the underlying patterns and distribution characteristics of molecules. According to the results in Table 2, data augmentation significantly outperforms training directly on the small linker dataset. In terms of uniqueness (0.587) and FCD score (6.96), it even surpasses transfer learning. This indicates that data augmentation enables the model to better capture the diverse representations of SMILES, ultimately leading to improved generation performance. (2) Effect of Different Pre-training Datasets  
The source and feature distribution of pre-training data have been shown to be significantly associated with a model's generalization ability and task-specific performance. To systematically investigate this phenomenon, this study utilized publicly available large-scale chemical datasets from different domains, including ChEMBL, which focuses on bioactivity screening; ZINC, which is oriented toward organic synthesis; and QM9, derived from quantum mechanical calculations [50]. As shown in Table 3, in the transfer learning tasks, pre-training on ChEMBL resulted in the best overall performance for linker generation. However, its performance in terms of novelty was slightly lower (0.968) than that of the other two datasets (0.998). This highlights the influence that different pre-training datasets can have on specific aspects of model behavior [51]. Figure 5 compares the chemical spaces of the original and generated linker datasets using the UMAP algorithm [52; 53; 54]. Each point in the plot represents a unique linker structure, and the spatial distribution illustrates the overall similarity among structures in high-dimensional chemical feature space. In Figure 5A, which displays the original linkers, the data points are tightly clustered, indicating high structural similarity and limited diversity among existing linkers. This pattern suggests that traditional linker design has primarily explored a narrow region of the available chemical space. In contrast, Figure 5B--showing the linkers generated by our method--reveals a much more dispersed distribution, with many points occupying regions not covered by the original linkers. This broader spread demonstrates that our approach generates structurally diverse and \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Valid** & **Unique** & **Novelty** & **FCD** & **KL** \\ \hline Linker\_Only & 0.146 & 0.973 & 1.00 & 15.63 & 0.71 \\ Linker\_Aug & 0.848 & 0.587 & 0.973 & 6.96 & 0.86 \\ Linker\_TL & 0.989 & 0.404 & 0.968 & 7.48 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effect of data augmentation on linker design. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Valid** & **Unique** & **Novelty** & **KL** & **FCD** \\ \hline ChEMBL & 0.989 & 0.404 & 0.968 & 0.88 & 7.48 \\ ZINC & 0.979 & 0.333 & 0.998 & 0.86 & 7.71 \\ QM9 & 0.975 & 0.331 & 0.998 & 0.86 & 7.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effect of different pre-training datasets. chemically novel linkers beyond conventional design principles. Overall, these results indicate that our method effectively expands the accessible chemical space for linkers, enabling the discovery of new structural motifs with potential functional advantages. The experimental results demonstrate that molecular representations significantly influence linker generation performance.