Dataset | SVMa | XGBoostb | RFb | DNNc | GCNd | GATd | MPNNd | Attentive FPd |
---|
FreeSolv (639) | 0.17 | 0.209 | 1.429 | 6.27 | 18.458 | 29.37 | 77.85 | 20.927 |
ESOL (1127) | 0.51 | 0.329 | 0.342 | 9.032 | 68.197 | 80.597 | 181.114 | 59.199 |
Lipop (4200) | 6.431 | 7.379 | 5.722 | 28.686 | 159.879 | 151.191 | 611.048 | 652.777 |
BACE (1513) | 2.105 | 0.327 | 1.327 | 8.911 | 108.967 | 156.074 | 630.748 | 137.291 |
BBBP (2035) | 8.033 | 0.242 | 0.873 | 6.74 | 83.062 | 129.817 | 316.224 | 98.743 |
HIV (40748) | 852.312 | 23.653 | 14.118 | 215.965 | 867.148 | 1122.126 | 1867.602 | 677.536 |
- aSVM was implemented with the scikit-learn package and run in a single thread (CPU: Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10 GHz); bXGBoost and RF were implemented with the scikit-learn package and run in six parallel threads (CPU: Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10 GHz); cDNN was implemented with PyTorch package and run in a single GPU card (NVIDIA GEFORCE RTX 2080 Ti with video memory of 11G); dGCN, GAT, MPNN and Attentive FP were implemented with DGL package using PyTorch as the backend and run in a single GPU card (NVIDIA GEFORCE RTX 2080 Ti with video memory of 11G); All tested NN-based models were trained with a batch-size 128 in early-stopping way as described in ‘Materials and methods’ (HIV with a batch-size 128*5 due to the large data volume)