title: Correcting the impact of docking pose generation error on binding affinity prediction.
abstract: BACKGROUND
  Pose generation error is usually quantified as the difference between the geometry of the pose generated by the docking software and that of the same molecule co-crystallised with the considered protein. Surprisingly, the impact of this error on binding affinity prediction is yet to be systematically analysed across diverse protein-ligand complexes.
  
  
  RESULTS
  Against commonly-held views, we have found that pose generation error has generally a small impact on the accuracy of binding affinity prediction. This is also true for large pose generation errors and it is not only observed with machine-learning scoring functions, but also with classical scoring functions such as AutoDock Vina. Furthermore, we propose a procedure to correct a substantial part of this error which consists of calibrating the scoring functions with re-docked, rather than co-crystallised, poses. In this way, the relationship between Vina-generated protein-ligand poses and their binding affinities is directly learned. As a result, test set performance after this error-correcting procedure is much closer to that of predicting the binding affinity in the absence of pose generation error (i.e. on crystal structures). We evaluated several strategies, obtaining better results for those using a single docked pose per ligand than those using multiple docked poses per ligand.
  
  
  CONCLUSIONS
  Binding affinity prediction is often carried out on the docked pose of a known binder rather than its co-crystallised pose. Our results suggest than pose generation error is in general far less damaging for binding affinity prediction than it is currently believed. Another contribution of our study is the proposal of a procedure that largely corrects for this error. The resulting machine-learning scoring function is freely available at http://istar.cse.cuhk.edu.hk/rf-score-4.tgz and http://ballester.marseille.inserm.fr/rf-score-4.tgz .
authors.affiliation: Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China.
authors.forename: Hongjian
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China.
authors.forename: Kwong-Sak
authors.initials: KS
authors.lastname: Leung
authors.affiliation: Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China.
authors.forename: Man-Hon
authors.initials: MH
authors.lastname: Wong
authors.affiliation: Cancer Research Center of Marseille, INSERM U1068, Marseille, F-13009, France. pedro.ballester@inserm.fr.|Institut Paoli-Calmettes, Marseille, F-13009, France. pedro.ballester@inserm.fr.|Aix-Marseille Université, Marseille, F-13284, France. pedro.ballester@inserm.fr.|CNRS UMR7258, Marseille, F-13009, France. pedro.ballester@inserm.fr.
authors.forename: Pedro J
authors.initials: PJ
authors.lastname: Ballester
mesh_terms: D006801:Humans; D008024:Ligands; D062105:Molecular Docking Simulation / Q000592:standards*; D009687:Nuclear Proteins / Q000737:chemistry / Q000378:metabolism*; D011485:Protein Binding; D011487:Protein Conformation; D011719:Pyrazines / Q000737:chemistry / Q000378:metabolism*; D012984:Software; D014157:Transcription Factors / Q000737:chemistry / Q000378:metabolism*
chemical_list: D008024:Ligands; D009687:Nuclear Proteins; C503859:PI3KCA protein, human; D011719:Pyrazines; D014157:Transcription Factors
keywords: Binding affinity; Drug discovery; Machine learning; Molecular docking