Table 1.
Top-performing pose predictions for BACE1 Stage 1a (A) and Stage 1b (B), based on median Pose 1 RMSD (Å). The standard deviations (SD RMSD) of the Pose 1 RMSDs are also provided as measure of scatter. Software lists the software listed by the participants in their protocol files. Submitter/PI: names of submitter and principal investigator (PI) provided with submission. Organization: institution of PI provided with submission. Visual Inspection lists the participant’s response to the standard question “Did you use visual inspection to select, eliminate, and/or manually adjust your final predicted poses?” Similar Ligands lists the participant’s response to the standard question “Did you use publicly available co-crystal structures of this protein with similar ligands to guide your pose predictions?”
| (A) | ||||||||
|---|---|---|---|---|---|---|---|---|
| Median RMSD | Mean RMSD | SD RMSD | Software | Submitter/PI | Organization | Visual Inspection | Similar Ligands | Submission ID |
| 0.55 | 0.81 | 0.58 | efindsite 1.3, openbabel 2.4.1, discover studio visualizer 4.5, maestro 10.2, shafts, gaussian 09, amber 16, homemade deep learning | K. Gao/G. Wei | Michigan State | yes | yes | 5t302 |
| 0.56 | 0.81 | 0.57 | efindsite 1.3, openbabel 2.4.1, discover studio visualizer 4.5, maestro 10.2, shafts, gaussian 09, amber 16, homemade deep learning | K. Gao/G. Wei | Michigan State | yes | yes | 0invp |
| 0.64 | 0.77 | 0.34 | brikard, libmol, rdkit, openbabel | D. Kozakov/D. Kozakov | Stony Brook | yes | yes | 4x5a8 |
| 0.72 | 0.94 | 0.65 | rdkit/torch/macromodel | Anonymous | Anonymous | yes | yes | uq8b0 |
| 0.74 | 0.93 | 0.45 | molsoft icm 3.8–7b | P. Lam/M. Totrov | Molsoft | no | yes | fky0k |
| 0.83 | 1.46 | 2.07 | cactvs chemoinformatics toolkit v3.433/schrodinger suite 2018–1/corina v3.60/ucsf chimera v1.10.2/gold v5.2 | I. Bogdan/I. Bogdan | Institut de Chimie des Substances Naturelles | no | yes | 0zdxk |
| 0.83 | 1.1 | 0.63 | cactvs chemoinformatics toolkit v3.433/schrodinger suite 2018–1/corina v3.60/ucsf chimera v1.10.2/gold v5.2 | I. Bogdan/I. Bogdan | Institut de Chimie des Substances Naturelles | no | yes | rapwf |
| 0.96 | 1.2 | 0.8 | Rosetta/corina classic, webserver version/openbabel-2.4.1/antechamber-17.3 | H. Park/ | University of Washington | no | yes | xdd3r |
| 1.02 | 1.33 | 0.71 | htmd1.13.8/acemd2/rdkit2018.03.4 | A. Rial/G. Fabritiis | Accelera | yes | yes | qqou3 |
| 1.02 | 1.06 | 0.57 | maestro/openeye/mgltools/autodock vina | X.Xu/X. Zou | University of Missouri-Columbia | yes | yes | t3ddc |
| (B) | ||||||||
|---|---|---|---|---|---|---|---|---|
| Median RMSD | Mean RMSD | SD RMSD | Software | Submitter/PI | Organization | Visual Inspection |
Similar Ligands | Submission ID |
| 0.56 | 0.61 | 0.23 | molsoft icm 3.8–7b | L.Polo/M.Totrov | Molsoft | No | no | 5od5g |
| 0.57 | 0.84 | 0.58 | efindsite 1.3, openbabel 2.4.1, discover studio visualizer 4.5, maestro 10.2, shafts, guassian 09, amber 16, homemade deep learning | K. Gao/G.Wei | Michigan State | yes | yes | 2ieqo |
| 0.59 | 0.85 | 0.56 | efindsite 1.3, openbabel 2.4.1, discover studio visualizer 4.5, maestro 10.2, shafts, guassian 09, amber 16, homemade deep learning | K. Gao/G.Wei | Michigan State | yes | yes | 4myne |
| 0.64 | 0.76 | 0.32 | brikard, libmol, rdkit, openbabel, vina, libsampling | D.Kozakov/D.Kozakov | Stonybrook | yes | yes | mwnwr |
| 0.66 | 0.84 | 0.63 | rdkit/torch/macromodel | Anonymous | Anonymous | no | yes | qaezm |
| 0.69 | 1.18 | 1.27 | moe2016.08/autodock4/mgltools1.5.7rc1/amber16/ligprep/rdkit2018–3 | M.Carlos/M.Marti | yes | yes | xd07v | |
| 0.71 | 0.92 | 1.01 | schrodinger/in-house deep learning | D.Nguyen/G.Wei | Michigan State | no | yes | bix32 |
| 0.71 | 0.74 | 0.37 | schrodinger/in-house deep learning | D.Nguyen/G.Wei | Michigan State | no | yes | itzv6 |
| 0.74 | 1.34 | 1.92 | openbabel 2.3.2 maestro 2018–3 prime smina apr 2 2016 | B.Wingert/C.Camacho | university of pittsburgh | yes | yes | ah0e6 |
| 0.76 | 0.89 | 0.35 | autodock vina with in-house modifications (convex-pl as a scoring function), rdkit 2018, scipy, pymol 1.8.4, unreleased version of convex-pl scoring function | M.Kadukova/S.Grudinin | inria grenoble, mipt moscow | no | yes | nyrou |