Table 3.
Comparison between Sfcnn and the top 10 scoring functions tested on the CASF-2016 benchmark
| Scoring function | R | SD | Size | Description |
|---|---|---|---|---|
| ΔVinaRF20 | 0.816 | 1.26 | 285 | Machine learning |
| Sfcnn | 0.792 | 1.32 | 283 | Machine learning |
| X-Score | 0.631 | 1.69 | 285 | Empirical |
| X-ScoreHS | 0.629 | 1.69 | 285 | Empirical |
| ΔSAS | 0.625 | 1.7 | 285 | Single descriptor |
| X-ScoreHP | 0.621 | 1.7 | 285 | Empirical |
| ASP@GOLD | 0.617 | 1.71 | 282 | Knowledge-based |
| ChemPLP@GOLD | 0.614 | 1.72 | 281 | Empirical |
| X-ScoreHM | 0.609 | 1.73 | 285 | Empirical |
| AutoDockVina | 0.604 | 1.73 | 285 | Empirical |
| DrugScore2018 | 0.602 | 1.74 | 285 | Knowledge-based |
Results (excluding Sfcnn) cited from Su et al. [14]. The performance of these scoring functions was recalculated by us for comparison