From: Random-forest model for drug–target interaction prediction via Kullback–Leibler divergence
Model | Test Set | Drug structure (Sim) | Similarity metric | Highest recall | AUC | Refs. |
---|---|---|---|---|---|---|
KLD-RF | 17 Targets in ChEMBL | Multiple Conformers (3D-Sim) | KLD vector from TC | 1.00 Average: 0.889 | Average: 0.992 HSP90: 0.998 | This Work |
CSNAP3D | 6 Targets in DUD | One Conformer with Lowest Energy (3D-Sim) | 28 including TC with cut-off 0.85 | 0.98 | AUC* 0.54—0.70 HSP90: 0.79 | Lo et al. [25] |
CSNAP2D** | 6 Targets in DUD | 2D Structure (2D-Sim) | TC with cut-off 0.6 | 0.83 | – | Lo et al. [24] |
SEA** | TC with cut-off 0.57 | 0.64 | 0.972*** | Keiser et al. [47] | ||
PASS** | Probability Function | 0.11 | – | Lagunin et al. [56] | ||
SwissTarget | 17 Targets in ChEMBL | 2D + 3D-Sim | Probability Function from 2 and 3D TC | 0.99 Average: 0.748 | Average: 0.869 | Gfeller et al. [58] |