# A Message Passing Approach to Biomedical Relation Classification for Drug-Drug Interactions
## 3 Results and Discussion
### Effectiveness of Non-Message Passing Units  
In addition to adopting the GIN framework to increase the expressiveness of the shallow GNN network, it has been further proposed to incorporate non-message passing units \(MLP_{pre}\) and \(MLP_{post}\) into the architecture [35]. The contribution of increased model complexity to the performance is confirmed by the significant improvement in the otherwise underperforming GraphSAGE (M1.1). Although not as pronounced, GIN models also appear to benefit. The performance metrics in Table 3 show the advantage of including the \(MLP_{post}\) unit in the M1-9.2 models over the basic M1-9.1 models. Adding the unit yields an average increase of 0.9% in each metric, with eight models achieving better \(P_{micro}\), \(R_{micro}\) scores, and seven models achieving better \(F1_{micro}\) scores. At the same time, the rest of the models where either precision or recall is affected, the opposite metric (\(P_{micro}\iff R_{micro}\)) shows an improvement in the order of 2.6% on average, which is always superior to the corresponding drop. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Model** & \multicolumn{2}{c}{**MLP**} & \multicolumn{4}{c}{**Metrics per Classification Target**} & \multicolumn{4}{c}{**Overall Metrics**} \\ \hline
**M** & **BERT-Based LM** & **Pre** & **Post** & **Advice** & **Effect** & **Int** & **Mech. ** & \(P_{micro}\) & \(R_{micro}\) & \(F1_{micro}\) \\ \hline
1.1 & & & & 70.51 & 67.04 & 48.21 & 58.39 & 71.59 & 57.37 & 63.70 \\
1.2 & SAGE BioBERT base & & x & 74.78 & 72.92 & 51.61 & 70.72 & 72.42 & 69.47 & 70.92 \\
1.3 & & x & x & 65.59 & 70.02 & 50.00 & 68.69 & 75.00 & 60.39 & 66.90 \\ \hline
2.1 & & & & 75.00 & 74.96 & 52.71 & 71.56 & 72.91 & 71.18 & 72.04 \\
2.2 & BioBERT base & & x & 76.84 & 76.09 & 52.71 & 71.49 & 73.16 & 72.58 & 72.87 \\
2.3 & & x & x & 82.45 & 73.46 & 52.80 & 74.27 & 73.42 & 74.87 & 74.14 \\ \hline
3.1 & & & & 79.14 & 76.19 & 52.71 & 71.46 & 72.81 & 71.28 & 72.04 \\
3.2 & BioBERT SLDA & & x & 84.45 & 75.46 & 53.80 & 76.27 & 75.81 & 74.13 & 74.96 \\
3.3 & & x & x & **86.45** & **78.46** & 54.80 & **82.27** & **84.33** & **75.55** & **79.14** \\ \hline
4.1 & & & & 72.30 & 73.14 & 40.88 & 67.94 & 66.67 & 70.53 & 68.54 \\
4.2 & BioBERT DLDA & & x & 72.53 & 73.36 & 40.88 & 70.02 & 66.91 & 71.84 & 69.29 \\
4.3 & & x & x & 71.94 & 70.43 & 39.37 & 69.22 & 63.78 & 72.76 & 67.98 \\ \hline
5.1 & & & & 69.23 & 67.83 & 15.50 & 70.66 & 62.97 & 66.45 & 64.66 \\
5.2 & BioBERT large & & x & 70.86 & 69.54 & 20.97 & 71.52 & 65.48 & 67.63 & 66.54 \\
5.3 & & x & x & 66.67 & 67.19 & 12.17 & 71.85 & 62.58 & 66.45 & 64.45 \\ \hline
6.1 & & & & 72.48 & 71.68 & 51.24 & 73.00 & 70.51 & 70.79 & 70.65 \\
6.2 & SciBERT & & x & 73.51 & 72.82 & 52.31 & 73.75 & 70.03 & 73.16 & 71.56 \\
6.3 & & x & x & 73.63 & 69.86 & 50.38 & 70.25 & 66.67 & 72.11 & 69.28 \\ \hline
7.1 & & & & 80.12 & 69.43 & 47.06 & 67.11 & 67.16 & 71.32 & 69.18 \\
7.2 & SciBERT SLDA & & x & 77.35 & 69.18 & 43.94 & 68.60 & 68.45 & 69.08 & 68.76 \\
7.3 & & x & x & 79.06 & 67.15 & 40.35 & 67.10 & 70.55 & 65.26 & 67.81 \\ \hline
8.1 & & & & 78.86 & 73.24 & 51.91 & 70.46 & 72.52 & 71.18 & 71.85 \\
8.2 & SciBERT DLDA & & x & 80.89 & 74.15 & 47.93 & 73.28 & 72.74 & 74.08 & 73.40 \\
8.3 & & x & x & 77.97 & 71.26 & 46.55 & 73.94 & 72.70 & 70.79 & 71.73 \\ \hline
9.1 & & & & 73.24 & 68.23 & 58.06 & 67.24 & 69.19 & 67.37 & 68.27 \\
9.2 & BERT base Conclusively, the three best performing models (M2.3, M3.3 and M8.2) include this unit. Each presents an improvement over its base version (M2.1, M3.1 and M8.1, respectively), while improving on the individual class level as well. Particularly, in at least three of the five classes, there is a clear improvement or at least some stability (decrease \(\leq\) 0.08). This behavior is partially confirmed by the other (underperforming) GIN models, although not universally. In contrast, the effect of the combination of the two units appears to be negative in the vast majority of models.