From: Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations
Test set | MCS size binned | Random order | CSD probability | Sage energy | SchNet | DimeNet++ | ComENet | TFD2SimRefMCS |
---|---|---|---|---|---|---|---|---|
Random | [0, 10[ | 0.35 ± 0.08 | 0.41 ± 0.14 | 0.4 ± 0.18 | 0.44 ± 0.11 | 0.48 ± 0.13 | 0.43 ± 0.12 | 0.37 ± 0.13 |
[10, 20[ | 0.16 ± 0.02 | 0.22 ± 0.03 | 0.26 ± 0.04 | 0.27 ± 0.02 | 0.28 ± 0.04 | 0.26 ± 0.03 | 0.23 ± 0.05 | |
[20, 30[ | 0.11 ± 0.02 | 0.15 ± 0.02 | 0.14 ± 0.02 | 0.21 ± 0.03 | 0.23 ± 0.05 | 0.3 ± 0.03 | 0.41 ± 0.07 | |
[30, 40[ | 0.04 ± 0.02 | 0.07 ± 0.03 | 0.09 ± 0.05 | 0.23 ± 0.08 | 0.31 ± 0.08 | 0.44 ± 0.1 | 0.48 ± 0.07 | |
[40, 50[ | 0.04 ± 0.04 | 0.12 ± 0.15 | 0.04 ± 0.05 | 0.13 ± 0.06 | 0.3 ± 0.12 | 0.43 ± 0.32 | 0.55 ± 0.17 | |
Scaffold | [0, 10[ | 0.15 ± 0.05 | 0.39 ± 0.28 | 0.33 ± 0.14 | 0.35 ± 0.21 | 0.44 ± 0.32 | 0.38 ± 0.17 | 0.5 ± 0.25 |
[10, 20[ | 0.16 ± 0.03 | 0.2 ± 0.04 | 0.23 ± 0.05 | 0.19 ± 0.07 | 0.22 ± 0.04 | 0.22 ± 0.04 | 0.21 ± 0.05 | |
[20, 30[ | 0.1 ± 0.01 | 0.13 ± 0.03 | 0.14 ± 0.02 | 0.13 ± 0.06 | 0.19 ± 0.06 | 0.25 ± 0.07 | 0.33 ± 0.07 | |
[30, 40[ | 0.05 ± 0.03 | 0.08 ± 0.01 | 0.1 ± 0.08 | 0.13 ± 0.07 | 0.21 ± 0.08 | 0.28 ± 0.09 | 0.35 ± 0.11 | |
[40, 50[ | 0.06 ± 0.07 | 0.05 ± 0.07 | 0.22 ± 0.36 | 0.18 ± 0.1 | 0.28 ± 0.25 | 0.48 ± 0.36 | 0.36 ± 0.42 |