Journal of Computer-Aided Molecular Design

, Volume 33, Issue 1, pp 47–59 | Cite as

Shape similarity guided pose prediction: lessons from D3R Grand Challenge 3

  • Ashutosh Kumar
  • Kam Y. J. ZhangEmail author


To extend the utility of ligand 3D shape similarity into pose prediction and virtual screening, we have previously developed CDVS and PoPSS methods. Both of them utilize ligand 3D shape similarity with the crystallographic ligands to improve pose prediction. While CDVS utilizes shape similarity to select suitable receptor structures for molecular docking, PoPSS places a ligand conformation of the highest shape similarity with crystal ligands into the target protein binding pocket which is then refined by side-chain repacking and Monte Carlo energy minimization. Analyses of PoPSS revealed some drawbacks in ligand conformation generation and the scoring scheme used. Moreover, as PoPSS does not sample the ligand conformation after placing it in the binding pocket, it relies solely on conformation generation methods to produce native like conformations. To address these limitations of PoPSS method, we report here a modified approach named as PoPSS-Lite, where side-chain repacking was replaced by a simple grid-based energy minimization. This modification also allowed the sampling of terminal functional groups while keeping the core scaffold fixed. Furthermore, shape similarity calculations were improved by increasing the number of ligand conformations and using a different similarity metric. The performance of PoPSS-Lite was prospectively evaluated in D3R GC3. Comparison of PoPSS-Lite demonstrated superior performance over PoPSS and CDVS with lower mean and median RMSDs. Furthermore, comparison with other D3R GC3 pose prediction submissions revealed top performance for PoPSS-Lite. Our D3R GC3 result extends our perspective that ligand 3D shape similarity with known crystallographic information can be successfully used to predict the binding pose of ligands with unknown binding modes. Our D3R GC3 results further highlight the necessity for improvement in conformer generation methods in order to improve shape similarity guided pose prediction.

Graphical abstract


Molecular docking Pose prediction Ligand 3D shape similarity Drug Design Data Resource D3R D3R Grand Challenge 3 



We acknowledge RIKEN ACCC for the supercomputing resources at the Hokusai BigWaterfall supercomputer used in this study. This research was supported by Platform Project for Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS)) from AMED under Grant Number JP18am0101082. We thank members of our lab for help and discussions.

Supplementary material

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Laboratory for Structural BioinformaticsCenter for Biosystems Dynamics Research, RIKENYokohamaJapan

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