Best Practices for Constructing, Preparing, and Evaluating Protein-Ligand Binding Affinity Benchmarks [Article v1.0]

  title={Best Practices for Constructing, Preparing, and Evaluating Protein-Ligand Binding Affinity Benchmarks [Article v1.0]},
  author={David F. Hahn and Christopher I. Bayly and Melissa L Boby and Hannah E. Bruce Macdonald and John D. Chodera and Vytautas Gapsys and Antonia Mey and David L. Mobley and Laura Perez Benito and Christina E. M. Schindler and Gary Tresadern and Gregory L. Warren},
  journal={Living Journal of Computational Molecular Science},
1Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium; 2OpenEye Scientific Software, 9 Bisbee Court, Suite D, Santa Fe, NM 87508 USA; 3Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA; 4MSD, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, United Kingdom; 5EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings… 
4 Citations

Precise Binding Free Energy Calculations for Multiple Molecules Using an Optimal Measurement Network of Pairwise Differences.

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Development and Benchmarking of Open Force Field v1.0.0-the Parsley Small-Molecule Force Field.

This initial Parsley force field affords accuracy similar to that of other general force fields when used to calculate relative binding free energies spanning 199 protein-ligand systems, and the resulting infrastructure allows us to rapidly optimize an entirely new force field with minimal human intervention.

Pre-Exascale Computing of Protein–Ligand Binding Free Energies with Open Source Software for Drug Design

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Chemical Space Exploration with Active Learning and Alchemical Free Energies.

This work explores how an active learning protocol can be combined with first-principles based alchemical free energy calculations to identify high affinity phosphodiesterase 2 (PDE2) inhibitors.



Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments

It is shown that database enrichment is improved with proper preparation and that neglecting certain steps of the preparation process produces a systematic degradation in enrichments, which can be large for some targets.

D3R Grand Challenge 2: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies

The outcome of GC2 underscores the pressing need for methods development in pose prediction, particularly for ligand scaffolds not currently represented in the Protein Data Bank (, and in affinity ranking and scoring of bound ligands.

Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field.

An approach to designing tight-binding ligands with a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches is reported, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.

D3R grand challenge 4: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies

The results of Grand Challenge 4, which focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges, are reported on.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

This work presents the results of large-scale prospective application of the FEP+ method in active drug discovery projects in an industry setting at Merck KGaA, Darmstadt, Germany and compares results obtained on a new diverse, public benchmark of eight pharmaceutically relevant targets.

D3R Grand Challenge 3: blind prediction of protein–ligand poses and affinity rankings

Grand Challenge 3, held 2017–2018, centered on the protein Cathepsin S and the kinases VEGFR2, JAK2, p38-α, TIE2, and ABL1, and included both pose-prediction and affinity-ranking components.

Predictions of Ligand Selectivity from Absolute Binding Free Energy Calculations

This work evaluated the performance of free energy calculations based on molecular dynamics for the prediction of selectivity by estimating the affinity profile of three bromodomain inhibitors across multiple bromidomain families, and by comparing the results to isothermal titration calorimetry data.

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An overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results is presented, with a focus on real-world drug discovery applications.

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A systematic characterization of the performance of free-energy perturbation (FEP) calculations to predict relative binding free energies of congeneric ligands binding to GPCR targets using a consistent protocol and no adjustable parameters is provided.

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In this work, an application for automated setup and processing of free energy calculations is presented and several sanity checks for assessing the reliability of the calculations were implemented, constituting a distinct advantage over existing open-source tools.