Table 2:
Summary of the reviewed scoring functions used in different models for predicting small molecule binding. a Some scoring functions optimized for protein may also be used for RNA. b Some models contain more than one scoring function, only the default one is listed. c The year that the original model was first published.
| Category | Model | Targeta | Score typeb | Yearc |
|---|---|---|---|---|
| Physics-based | MORDOR (64) | RNA | force fields | 2008 |
| DOCK 6 (65) | RNA | force fields | 2009 | |
| GOLD (59) | protein | empirical terms | 1997 | |
| Glide (61) | protein | empirical terms | 2004 | |
| RiboDock (60) | RNA | empirical terms | 2004 | |
| AutoDock 4 (63) | protein | empirical terms | 2007 | |
| AutoDock Vina (66) | protein | empirical terms | 2010 | |
| iMDLScore1 (57) iMDLScore2 (57) |
RNA | empirical terms | 2012 | |
| rDock (67) | protein nucleic acid | empirical terms | 2014 | |
| RLDOCK (70; 71) | RNA | empirical terms | 2020 | |
| Knowledge-based | DrugScoreRNA (112; 173) | RNA | statistical potentials | 2000 |
| KScore (174) | protein nucleic acid | statistical potentials | 2008 | |
| LigandRNA (175) | RNA | statistical potentials | 2013 | |
| SPA-LN (176) | nucleic acid | iterative statistical potentials | 2017 | |
| ITScore-NL (142) | nucleic acid | iterative statistical potentials | 2020 | |
| Machine-learning | T-Bind (177) | protein | gradient boosting trees | 2018 |
| RNAPosers (141) | RNA | random forest | 2020 | |
| RNAmigos (178) | RNA | graph neural network | 2020 |