Journal of Computer-Aided Molecular Design

, Volume 33, Issue 1, pp 35–46 | Cite as

Hybrid receptor structure/ligand-based docking and activity prediction in ICM: development and evaluation in D3R Grand Challenge 3

  • Polo C.-H. Lam
  • Ruben Abagyan
  • Maxim TotrovEmail author


In context of D3R Grand Challenge 3 we have investigated several ligand activity prediction protocols that combined elements of a physics-based energy function (ICM VLS score) and the knowledge-based Atomic Property Field 3D QSAR approach. Activity prediction models utilized poses produced by ICM-Dock with ligand bias and 4D receptor conformational ensembles (LigBEnD). Hybrid APF/P (APF/Physics) models were superior to pure physics- or knowledge-based models in our preliminary tests using rigorous three-fold clustered cross-validation and later proved successful in the blind prediction for D3R GC3 sets, consistently performing well across four different targets. The results demonstrate that knowledge-based and physics-based inputs into the machine-learning activity model can be non-redundant and synergistic.


D3R D3R GC3 ICM APF 3D QSAR Docking Computer-aided drug design 



Protein data bank


Atomic property field



The authors thank D3R organizers for coordinating the challenge. We also thank Eugene Raush for technical assistance, and Andrew Orry for proofreading of this manuscript.

Author contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare no competing financial interest.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Molsoft L.L.C.San DiegoUSA
  2. 2.Skaggs School of Pharmacy and Pharmaceutical SciencesUniversity of California San DiegoLa JollaUSA

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