Function | PCC | RMSE | Description | Year |
---|---|---|---|---|
RF-Score-v2 | 0.803a | 1.54 | Machine learning | 2014 |
ID-Score | 0.753b | 1.63 | Descriptor-based and empirical | 2013 |
ΔvinaRF20 | 0.686c | 1.64 | Machine learning | 2016 |
AutoDockHybrid | 0.64 | n.a. | Force fields and machine learning | 2016 |
X-ScoreHM | 0.614 | 1.78 | Empirical | 2002 |
ΔSASA | 0.606 | 1.79 | Empirical | 2014 |
ChemScore@SYBYL | 0.592 | 1.82 | Empirical | 1998 |
ChemPLP@GOLD | 0.579 | 1.84 | Empirical | 2009 |
DLIGAND2 | 0.572 | 1.85 | Knowledge-based | This paper |
SMoG2016 | 0.57d | 1.68 | Knowledge-based and empirical | 2016 |
PLP1@DS | 0.568 | 1.86 | Empirical | 2000 |
AutoDock Vina | 0.563e | 1.87 | Knowledge-based and empirical | 2010 |
G-Score@SYBYL | 0.558 | 1.87 | Energy-based | 1997 |
ASP@GOLD | 0.556 | 1.88 | Statistical potential | 2005 |
ASE@MOE | 0.544 | 1.89 | Empirical | n.a. |
ChemScore@GOLD | 0.536 | 1.90 | Empirical | 2003 |
DLIGAND | 0.526 | 1.92 | Knowledge-based | 2005 |
D-Score@SYBYL | 0.526 | 1.92 | Energy-based | 2001 |
Alpha-HB@MOE | 0.511 | 1.94 | Empirical | n.a. |
LUDI3@DS | 0.487 | 1.97 | Empirical | 1998 |
GoldScore@GOLD | 0.483 | 1.97 | Energy-based | 1997 |
Affinity-dG@MOE | 0.482 | 1.98 | Empirical | n.a. |
LigScore2@DS | 0.456 | 2.02 | Empirical | 2005 |
GlideScore-SP | 0.452 | 2.03 | Energy-based | 2006 |
SMoG2001 | 0.418 | 3.39 | Knowledge-based | 2001 |
Jain@DS | 0.408 | 2.05 | Empirical | 2006 |
PMF@DS | 0.364 | 2.11 | Statistical potential | 2006 |
GlideScore-XP | 0.277 | 2.18 | Energy-based | 2004 |
London-dG@MOE | 0.242 | 2.19 | Empirical | n.a. |
PMF@SYBYL | 0.221 | 2.20 | Statistical potential | 1999 |