Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation

@article{Grudinin2016PredictingBP,
  title={Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation},
  author={Sergei Grudinin and Maria Kadukova and Andreas Eisenbarth and Simon Marillet and Fr{\'e}d{\'e}ric Cazals},
  journal={Journal of Computer-Aided Molecular Design},
  year={2016},
  volume={30},
  pages={791-804}
}
The 2015 D3R Grand Challenge provided an opportunity to test our new model for the binding free energy of small molecules, as well as to assess our protocol to predict binding poses for protein-ligand complexes. Our pose predictions were ranked 3–9 for the HSP90 dataset, depending on the assessment metric. For the MAP4K dataset the ranks are very dispersed and equal to 2–35, depending on the assessment metric, which does not provide any insight into the accuracy of the method. The main success… 
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