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. Author manuscript; available in PMC: 2020 Jan 10.
Published in final edited form as: J Comput Aided Mol Des. 2019 Jan 10;33(1):1–18. doi: 10.1007/s10822-018-0180-4

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