Table 1.
Results for Phase 1 are given for all 52 methods submitted by the 29 participating research groups. The columns note the number of structures for which each method was able to identify the near-native pose as the top-scoring pose and in the top-3 poses. Bold numbers in the last two columns highlight the methods that were able to score at least 20 of the structures correctly. The list of Phase 1 participants is ordered by the total number of structures where the near-native pose is their top score, but the IDs in the first columns simply reflects the order that submissions were received.
| Participants IDa |
TrmD (14 Structures) |
SYK (5 Structures) |
FXa (3 Structures) |
Composite (All 22) |
Pose-Scoring Method The order of this list is not intended to declare one method better than another. More structures are needed for a statistically significant assessment. |
||||
|---|---|---|---|---|---|---|---|---|---|
| Top Score |
Top 3 | Top Score |
Top 3 | Top Score |
Top 3 | Top Score |
Top 3 | ||
| p5 | 14 | 14 | 5 | 5 | 3 | 3 | 22 | 22 | Autodock-Vina12 implemented in SMINA6,13 |
| p6 | 14 | 14 | 5 | 5 | 3 | 3 | 22 | 22 | GoldScore5 (TrmD, Syk), RMSD-based function (FXa)78 |
| p7 | 14 | 14 | 5 | 5 | 3 | 3 | 22 | 22 | PELE7 + GB solvation energy83 + ligand-strain term + conformational entropy term84 |
| p14 | 14 | 14 | 5 | 5 | 3 | 3 | 22 | 22 | GBVI/WSA (MOE)91 / ChemPLP3 |
| p17 | 14 | 14 | 5 | 5 | 3 | 3 | 22 | 22 | SIE4,77 |
| p19 | 14 | 14 | 5 | 5 | 3 | 3 | 22 | 22 | Rosetta Talaris201392,93 |
| p20 | 14 | 14 | 5 | 5 | 3 | 3 | 22 | 22 | Customized empirical scoring60 |
| p8 | 13 | 14 | 5 | 5 | 3 | 3 | 21 | 22 | MedusaScore75,79 |
| p12-2 | 13 | 14 | 5 | 5 | 3 | 3 | 21 | 22 | Unknownb |
| p22 | 13 | 14 | 5 | 5 | 3 | 3 | 21 | 22 | Glide-XP2 |
| p26 | 14 | 14 | 5 | 5 | 2 | 3 | 21 | 22 | Hybrid Autodock61/Autodock-Vina12 |
| p27-4 | 14 | 14 | 5 | 5 | 2 | 3 | 21 | 22 | Autodock-Vina12,81 |
| p9-9 | 13 | 13 | 5 | 5 | 3 | 3 | 21 | 21 | Chemgauss465/FRED66 |
| p9-10 | 13 | 13 | 5 | 5 | 3 | 3 | 21 | 21 | Chemgauss4/FRED65 with rigid optimization of ligand |
| p4 | 12 | 13 | 5 | 5 | 3 | 3 | 20 | 21 | Machine Learning scoring model MARS94 |
| p27-2 | 13 | 14 | 4 | 4 | 3 | 3 | 20 | 21 | ITScore1,10 (refit to refined PDBbind 2012)81 |
| p13 | 12 | 12 | 5 | 5 | 3 | 3 | 20 | 20 | Unknownb |
| p30-2 | 14 | — | 3 | — | 3 | — | 20 | — | HYDE14 + visual inspection (lab provided only top poses) |
| p27-3 | 11 | 14 | 5 | 5 | 3 | 3 | 19 | 22 | ITScore1,10 (modified)81 |
| p9-8 | 10 | 14 | 5 | 5 | 3 | 3 | 18 | 22 | SZMAP63 (formal charge/element/bonding for atom typing) |
| p28 | 10 | 14 | 5 | 5 | 3 | 3 | 18 | 22 | SPA95 |
| p21 | 8 | 14 | 5 | 5 | 3 | 3 | 16 | 22 | ITScore1,10 (plus flexible ligand term)11 |
| p9-1 | 9 | 13 | 4 | 5 | 3 | 3 | 16 | 21 | SZMAP63 (AM1BCC charge for atom typing) |
| p29 | 8 | 12 | 5 | 5 | 3 | 3 | 16 | 20 | Consensus80 (GalaxyDock96, X-Score74, DrugScore73) |
| p15-1 | 11 | 12 | 2 | 3 | 3 | 3 | 16 | 18 | ROCS15 shape similarity (best score)16 |
| p25-5 | 9 | 12 | 4 | 4 | 2 | 3 | 15 | 19 | Pharmacophore-based scoring with LigandScout97 |
| p25-1 | 6 | 12 | 5 | 5 | 3 | 3 | 14 | 20 | DSX-DrugScore73 |
| p30-1 | 12 | 13 | 2 | 3 | 0 | 1 | 14 | 17 | HYDE14 |
| p27-1 | 7 | 10 | 4 | 4 | 2 | 3 | 13 | 17 | ITScore1,10,81 |
| p9-3 | 10 | 13 | 2 | 4 | 0 | 1 | 12 | 18 | MMPBSA/Szybki64 (intRlx options) |
| p9-4 | 10 | 13 | 2 | 3 | 0 | 1 | 12 | 17 | MMPBSA/Szybki64 (intRst options) |
| p9-7 | 10 | 11 | 2 | 5 | 0 | 1 | 12 | 17 | MMPBSA/Szybki64 (totRst options) |
| p9-6 | 10 | 11 | 2 | 4 | 0 | 1 | 12 | 16 | MMPBSA/Szybki64 (totRlx options) |
| p23-1 | 5 | 7 | 4 | 5 | 3 | 3 | 12 | 15 | FLM98,99 |
| p9-5 | 7 | 11 | 3 | 4 | 1 | 1 | 11 | 16 | MMPBSA/Szybki64 (totInp options) |
| p9-2 | 7 | 12 | 2 | 4 | 1 | 1 | 10 | 17 | MMPBSA/Szybki64 (intInp options) |
| p25-3 | 6 | 7 | 0 | 0 | 2 | 2 | 8 | 9 | ChemPLP3 |
| p18-2 | 0 | 0 | 5 | 5 | 3 | 3 | 8 | 8 | Rank from Glide-SP100,101 + Rank from Random Forest model102 |
| p18-3 | 0 | 0 | 4 | 5 | 2 | 3 | 6 | 8 | Score from Glide-SP100,101 + Score Random Forest model102 |
| p18-4 | 0 | 0 | 4 | 4 | 2 | 3 | 6 | 7 | Glide-SP100,101 |
| p16 | 5 | 7 | 0 | 3 | 0 | 3 | 5 | 13 | Autodock-Vina12 + occupational probability score |
| p25-4 | 2 | 3 | 2 | 4 | 1 | 2 | 5 | 9 | ChemPLP3 (only non-bonded) |
| p18-1 | 0 | 0 | 3 | 4 | 2 | 2 | 5 | 6 | QSAR (Random Forest)102 |
| p15-2 | 3 | 7 | 0 | 0 | 1 | 2 | 4 | 9 | ROCS15 shape similarity (average score)16 |
| p24 | 0 | 1 | 2 | 3 | 2 | 2 | 4 | 6 | Unknownb |
| p2 | 0 | 1 | 0 | 1 | 3 | 3 | 3 | 5 | Boost Decision Tree scoring model67–69 |
| p1 | 0 | 0 | 1 | 2 | 0 | 1 | 1 | 3 | Scoring functions from Agostino et. al103 |
| p23-2 | 0 | 0 | 1 | 2 | 0 | 0 | 1 | 2 | SOL98,99 |
| p10 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 3 | Unknownb |
| p11 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | Unknown b |
| p12-1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | Unknownb |
| p25-2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | Elekit104 |
Dashed numbers denote alternate methods from the same group. Many participants submitted more than one set of predictions to compare different approaches and parameters.
Some participants were unable to provide details about their methods.