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Top 3 submissions, based on Kendall’s τ, for each affinity ranking challenge. Submission ID in bold font indicates a method that used machine learning. See for details.
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Top 3 submissions, based on Kendall’s τ, for each affinity ranking challenge. Submission ID in bold font indicates a method that used machine learning. See for details.
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Top 3 submissions, based on Kendall’s τ, for each affinity ranking challenge. Submission ID in bold font indicates a method that used machine learning. See for details.
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ASM International binary alloy phase diagrams
Top 3 submissions, based on Kendall’s τ, for each affinity ranking challenge. Submission ID in bold font indicates a method that used machine learning. See for details.
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Top 3 submissions, based on Kendall’s τ, for each affinity ranking challenge. Submission ID in bold font indicates a method that used machine learning. See for details.
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BioDiscovery Inc circular binary segmentation (cbs) algorithm
Top 3 submissions, based on Kendall’s τ, for each affinity ranking challenge. Submission ID in bold font indicates a method that used machine learning. See for details.
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Image Search Results


Top 3 submissions, based on Kendall’s τ, for each affinity ranking challenge. Submission ID in bold font indicates a method that used machine learning. See for details.

Journal: Journal of computer-aided molecular design

Article Title: D3R Grand Challenge 3: Blind Prediction of Protein-Ligand Poses and Affinity Rankings

doi: 10.1007/s10822-018-0180-4

Figure Lengend Snippet: Top 3 submissions, based on Kendall’s τ, for each affinity ranking challenge. Submission ID in bold font indicates a method that used machine learning. See for details.

Article Snippet: 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.

Techniques: Software

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 for details.

Journal: Journal of computer-aided molecular design

Article Title: D3R Grand Challenge 3: Blind Prediction of Protein-Ligand Poses and Affinity Rankings

doi: 10.1007/s10822-018-0180-4

Figure Lengend Snippet: 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 for details.

Article Snippet: 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.

Techniques: Software