Table 4 Performance comparison on PDBBind 2016
From: Force field-inspired molecular representation learning for property prediction
Method | PDBBind 2016 | ||||
|---|---|---|---|---|---|
RMSE ↓ | MAE ↓ | SD ↓ | R ↑ | ||
ML-based methods | RF | 1.446 (0.008) | 1.161 (0.007) | 1.335 (0.010) | 0.789 (0.003) |
SVR | 1.555 (0.000) | 1.264 (0.000) | 1.493 (0.000) | 0.727 (0.000) | |
CNN-based methods | OnionNet | 1.407 (0.034) | 1.078 (0.028) | 1.391 (0.038) | 0.768 (0.014) |
Pafnucy | 1.585 (0.013) | 1.284 (0.021) | 1.563 (0.022) | 0.695 (0.011) | |
GraphDTA methods | GCN | 1.735 (0.034) | 1.343 (0.037) | 1.719 (0.027) | 0.613 (0.016) |
GAT | 1.765 (0.026) | 1.354 (0.033) | 1.740 (0.027) | 0.601 (0.016) | |
GIN | 1.640 (0.044) | 1.261 (0.044) | 1.621 (0.036) | 0.667 (0.018) | |
GAT-GCN | 1.562 (0.022) | 1.191 (0.016) | 1.558 (0.018) | 0.697 (0.008) | |
GNN-based methods | DMPNN | 1.493 (0.016) | 1.188 (0.009) | 1.489 (0.014) | 0.729 (0.006) |
DimeNet | 1.453 (0.027) | 1.138 (0.026) | 1.434 (0.023) | 0.752 (0.010) | |
SIGN | 1.316 (0.031) | 1.027 (0.025) | 1.312 (0.035) | 0.797 (0.012) | |
Ours | FFiNet | 1.310 (0.012) | 1.056 (0.006) | 1.304 (0.014) | 0.801 (0.005) |