Table 6.
Top 3 submissions, based on Matthews correlation coefficient, for each affinity ranking challenge. Submission ID in bold font indicates a method that used machine learning. See Table 3 for details.
| Matthews Corr. Coeff. | Software | Submitter/PI | Organization | Submission ID |
|---|---|---|---|---|
| ABL1 | ||||
| 1.0 | rhodium 380e9–x9/openbabel 2.3.90 / pymol 1.3 | J. Bohmann/Medicinal and Process Chemistry | Southwest Research Institute | 3o8xi |
| 0.52 | rhodium 380e9–x9/openbabel 2.3.90 / pymol 1.3 | J. Bohmann/Medicinal and Process Chemistry | Southwest Research Institute | ktfzk |
| 0.52 | ri-score/tdl-bp/autodock vina | D. Nguyen/W. Guo-Wei | Michigan State University | c4xt7 |
| JAK2 SC2 | ||||
| 0.49 | molsoft icm 3.8–6 | P. Lam/M.Totrov | Molsoft | 7yjh3 |
| 0.49 | chemminer, libsvm 3.21 | A. Bonvin/A. Bonvin | Utrecht University | nzud3 |
| 0.44 | docking performed with gnina commit b3fa6ae13fc6b42924f49b2d751d68f1bc14bc08 available from https//github.com/gnina/gnina, conformer generation performed with rdkit via https//github.com/dkoes/rdkit-scripts/rdconf.py, ensemble of receptors chosen via pocketome. | J. Sunseri/D. Koes | University of Pittsburgh | j4kto |
| JAK2 SC3 | ||||
| 0.48 | itscore2 | X.Zou/X. Zou | University of Missouri-Columbia | 87mci |
| 0.23 | docking performed with smina static binary available at https//sourceforge.net/projects/smina/files/ with default scoring function, then rescoring performed using gnina commit b3fa6ae13fc6b42924f49b2d751d68f1bc14bc08 available from https//github.com/gnina/gnina and the default cnn scoring model, conformer generation performed with rdkit via https//github.com/dkoes/rdkit-scripts/rdconf.py, ensemble of receptors chosen via pocketome. | J. Sunseri/D. Koes | University of Pittsburgh | vshma |
| 0.23 | docking performed with gnina commit b3fa6ae13fc6b42924f49b2d751d68f1bc14bc08 available from https//github.com/gnina/gnina, conformer generation performed with rdkit via https//github.com/dkoes/rdkit-scripts/rdconf.py, ensemble of receptors chosen via pocketome. | J. Sunseri/D. Koes | University of Pittsburgh | 7bi2k |
| p38-α | ||||
| 0.49 | smina feb 28 2016, based on autodock vina 1.1.2 openbabel 2.3.2 pymol 1.8.4.2 python 2.7.11 matplotlib 1.5.1 scipy 0.17.0 click 6.6 | B. Wingert/C. Camacho | University of Pittsburgh | 5dbkc |
| 0.43 | molsoft icm 3.8–6 | P. Lam/M. Totrov | Molsoft | 8b6kk |
| 0.26 | amber11 | X. Zou/X. Zou | University of Missouri-Columbia | vkpk7 |
| TIE2 | ||||
| 1.0 | rhodium 380e9–x9/openbabel 2.3.90 / pymol 1.3 | J. Bohmann/Medicinal and Process Chemistry | Southwest Research Institute | mey8v |
| 0.78 | docking performed with smina static binary available at https//sourceforge.net/projects/smina/files/ with default scoring function, then rescoring performed using gnina commit b3fa6ae13fc6b42924f49b2d751d68f1bc14bc08 available from https//github.com/gnina/gnina and the default cnn affinity model, conformer generation performed with rdkit via https//github.com/dkoes/rdkit-scripts/rdconf.py, ensemble of receptors chosen via pocketome. | J. Sunseri/D. Koes | University of Pittsburgh | xpmn7 |
| 0.78 | itscore2 | X. Zou/X. Zou | University of Missouri-Columbia | fn2qt |
| VEGFR2 | ||||
| 0.53 | docking performed with smina static binary available at https//sourceforge.net/projects/smina/files/ with default scoring function, then rescoring performed using gnina commit b3fa6ae13fc6b42924f49b2d751d68f1bc14bc08 available from https//github.com/gnina/gnina and the default cnn scoring model, conformer generation performed with rdkit via | J. Sunseri/D. Koes | University of Pittsburgh | 8civr |
| https//github.com/dkoes/rdkit-scripts/rdconf.py, ensemble of receptors chosen via pocketome. | ||||
| 0.48 | ri-score/tdl-bp/autodock vina | D. Nguyen/W. Guo-Wei | Michigan State University | rtv8m |
| 0.48 | ri-score/tdl-bp/autodock vina | D. Nguyen/W. Guo-Wei | Michigan State University | qikvs |