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

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Abstract

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.

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Abbreviations

PDB:

Protein data bank

APF:

Atomic property field

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Acknowledgements

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.

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Authors and Affiliations

  1. Molsoft L.L.C., 11199 Sorrento Valley Road, S209, San Diego, CA, 92121, USA

    Polo C.-H. Lam & Maxim Totrov

  2. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA

    Ruben Abagyan

Authors
  1. Polo C.-H. Lam
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  2. Ruben Abagyan
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  3. Maxim Totrov
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The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

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Correspondence to Maxim Totrov.

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The authors declare no competing financial interest.

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Lam, P.CH., Abagyan, R. & Totrov, M. Hybrid receptor structure/ligand-based docking and activity prediction in ICM: development and evaluation in D3R Grand Challenge 3. J Comput Aided Mol Des 33, 35–46 (2019). https://doi.org/10.1007/s10822-018-0139-5

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  • DOI: https://doi.org/10.1007/s10822-018-0139-5

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