Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges

Bentley M. Wingert, Rick Oerlemans, Carlos J. Camacho*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)

Abstract

The goal of virtual screening is to generate a substantially reduced and enriched subset of compounds from a large virtual chemistry space. Critical in these efforts are methods to properly rank the binding affinity of compounds. Prospective evaluations of ranking strategies in the D3R grand challenges show that for targets with deep pockets the best correlations (Spearman rho similar to 0.5) were obtained by our submissions that docked compounds to the holo-receptors with the most chemically similar ligand. On the other hand, for targets with open pockets using multiple receptor structures is not a good strategy. Instead, docking to a single optimal receptor led to the best correlations (Spearman rho similar to 0.5), and overall performs better than any other method. Yet, choosing a suboptimal receptor for crossdocking can significantly undermine the affinity rankings. Our submissions that evaluated the free energy of congeneric compounds were also among the best in the community experiment. Error bars of around 1 kcal/mol are still too large to significantly improve the overall rankings. Collectively, our top of the line predictions show that automated virtual screening with rigid receptors perform better than flexible docking and other more complex methods.

Original languageEnglish
Pages (from-to)287-297
Number of pages11
JournalJournal of Computer-Aided Molecular Design
Volume32
Issue number1
DOIs
Publication statusPublished - Jan-2018

Keywords

  • D3R
  • Drug Design Data Resource
  • Virtual screening
  • Affinity ranking
  • Pose prediction
  • CSAR BENCHMARK EXERCISE
  • PROTEIN DATA-BANK
  • INVERSE AGONISTS
  • FORCE-FIELD
  • DOCKING
  • DISCOVERY
  • DATABASE
  • DESIGN

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