From: Application of 3D Zernike descriptors to shape-based ligand similarity searching
Comparison Method | Â | Â | Â | Â | ||
---|---|---|---|---|---|---|
Descriptors | Metric | Order | Adjusted Rand Index | BEDROC (α = 160.9) | BEDROC (α = 32.2) | ROC AUC |
3DZD | Correlation coefficient | 4 | 0.299 | 0.506 | 0.453 | 0.694 |
 |  | 6 | 0.256 | 0.590 | 0.494 | 0.708 |
 |  | 8 | 0.422 | 0.739 | 0.654 | 0.738 |
 |  | 10 | 0.465 | 0.614 | 0.536 | 0.724 |
 |  | 12 | 0.487 | 0.697 | 0.630 | 0.732 |
 |  | 14 | 0.442 | 0.697 | 0.618 | 0.733 |
 | Euclidean | 4 | 0.278 | 0.338 | 0.329 | 0.680 |
 |  | 6 | 0.278 | 0.526 | 0.446 | 0.704 |
 |  | 8 | 0.357 | 0.678 | 0.621 | 0.738 |
 |  | 10 | 0.395 | 0.594 | 0.553 | 0.722 |
 |  | 12 | 0.487 | 0.658 | 0.610 | 0.730 |
 |  | 14 | 0.372 | 0.717 | 0.622 | 0.743 |
 | Manhattan | 4 | 0.270 | 0.318 | 0.329 | 0.686 |
 |  | 6 | 0.260 | 0.484 | 0.427 | 0.703 |
 |  | 8 | 0.328 | 0.698 | 0.619 | 0.732 |
 |  | 10 | 0.408 | 0.591 | 0.619 | 0.736 |
 |  | 12 | 0.393 | 0.637 | 0.591 | 0.732 |
 |  | 14 | 0.213 | 0.656 | 0.598 | 0.748 |
USR | Correlation coefficient | 12 | 0.213 | 0.697 | 0.617 | 0.695 |
 |  | 16 (Kurtosis) | 0.227 | 0.721 | 0.651 | 0.707 |
 | Euclidean | 12 | 0.270 | 0.760 | 0.639 | 0.708 |
 |  | 16 | 0.270 | 0.760 | 0.642 | 0.709 |
 | Manhattan | 12 | 0.343 | 0.762 | 0.661 | 0.718 |
 |  | 16 | 0.328 | 0.782 | 0.675 | 0.720 |
SIMCOMP | (Maximal Common Subgraph) | - | 0.400 | 0.847 | 0.779 | 0.808 |
EVA | - | σ = 100 cm-1 | 0.442 | - | - | - |
 |  | σ = 50 cm-1 | 0.388 | - | - | - |
 |  | σ = 20 cm-1 | 0.381 | - | - | - |
UNITY2D | - | - | 0.247 | - | - | - |
MACCS | (Tanimoto) | 166 bit key | 0.364 | 0.778 | 0.659 | 0.742 |
MOLPRINT2D | (Tanimoto) | Â | 0.516 | 0.848 | 0.755 | 0.806 |
Atom Count | - | Â | 0.400 | 0.467 | 0.460 | 0.850 |