Journal of Computer-Aided Molecular Design

, Volume 32, Issue 10, pp 937–963 | Cite as

Overview of the SAMPL6 host–guest binding affinity prediction challenge

  • Andrea Rizzi
  • Steven Murkli
  • John N. McNeill
  • Wei Yao
  • Matthew Sullivan
  • Michael K. Gilson
  • Michael W. Chiu
  • Lyle Isaacs
  • Bruce C. Gibb
  • David L. MobleyEmail author
  • John D. ChoderaEmail author


Accurately predicting the binding affinities of small organic molecules to biological macromolecules can greatly accelerate drug discovery by reducing the number of compounds that must be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the accuracy of current computational approaches to affinity prediction against binding data to biological macromolecules is frustrated by several challenges, such as slow conformational dynamics, multiple titratable groups, and the lack of high-quality blinded datasets. Over the last several SAMPL blind challenge exercises, host–guest systems have emerged as a practical and effective way to circumvent these challenges in assessing the predictive performance of current-generation quantitative modeling tools, while still providing systems capable of possessing tight binding affinities. Here, we present an overview of the SAMPL6 host–guest binding affinity prediction challenge, which featured three supramolecular hosts: octa-acid (OA), the closely related tetra-endo-methyl-octa-acid (TEMOA), and cucurbit[8]uril (CB8), along with 21 small organic guest molecules. A total of 119 entries were received from ten participating groups employing a variety of methods that spanned from electronic structure and movable type calculations in implicit solvent to alchemical and potential of mean force strategies using empirical force fields with explicit solvent models. While empirical models tended to obtain better performance than first-principle methods, it was not possible to identify a single approach that consistently provided superior results across all host–guest systems and statistical metrics. Moreover, the accuracy of the methodologies generally displayed a substantial dependence on the system considered, emphasizing the need for host diversity in blind evaluations. Several entries exploited previous experimental measurements of similar host–guest systems in an effort to improve their physical-based predictions via some manner of rudimentary machine learning; while this strategy succeeded in reducing systematic errors, it did not correspond to an improvement in statistical correlation. Comparison to previous rounds of the host–guest binding free energy challenge highlights an overall improvement in the correlation obtained by the affinity predictions for OA and TEMOA systems, but a surprising lack of improvement regarding root mean square error over the past several challenge rounds. The data suggests that further refinement of force field parameters, as well as improved treatment of chemical effects (e.g., buffer salt conditions, protonation states), may be required to further enhance predictive accuracy.


SAMPL6 Host–guest Blind challenge Binding affinity Free energy Cucurbit[8]uril Octa-acid 



Austin model 1 bond charge correction [58, 59]


Atomic multipole optimized energetics for biomolecular simulation [103]


Becke 3-parameter Lee-Yang-Parr exchange-correlation functional [13]


Becke 3-parameter Perdew-Wang 91 exchange-correlation functional [13]


CHARMM generalized force field [129]


Conductor-like screening model for real solvents [65]


Double decoupling method [41]


Density functional theory with the D3 dispersion corrections [44]


Force matching [30]


Fast switching double annihilation method [97, 104]


Generalized AMBER force field [130]


Hamiltonian replica exchange [122]


Knowledge-based and empirical combined scoring algorithm [138]


KECSA-movable type implicit solvation model [140]


Molecular dynamics


Molecular mechanics Poisson Boltzmann/solvent accessible surface area [119]


Movable type method [139]


optimized potential for liquid simulations [48]


Poisson–Boltzmann surface area [114]


PM6 semiempirical method with dispersion and hydrogen bonding corrections [68, 108]


Restrained electrostatic potential [12]


Replica exchange with solute torsional tempering [73, 76]


Relative free energy calculation


Mixed quantum mechanics and molecular mechanics


Double annihilation or decoupling method performed with Sire/OpenMM6.3 software [28, 133]


Semi-empirical quantum mechanics


Transferable interaction potential three-point [61]


Tao, Perdew, Staroverov, and Scuseria exchange functional [125]


Umbrella sampling [128]


VSGB2.0 solvation model refit to OPLS2.1/3/3e [72]



AR and JDC acknowledge support from the Sloan Kettering Institute. JDC acknowledges support from NIH Grant No. P30CA008748. JDC, AR, and DLM gratefully acknowledge support from NIH Grant No. R01GM124270 supporting SAMPL blind challenges. AR acknowledges partial support from the Tri-Institutional Program in Computational Biology and Medicine. LI thanks the National Science Foundation for supporting (Grant No. CHE-1404911) the participation in SAMPL6. DLM appreciates financial support from the National Institutes of Health (Grant No. 1R01GM108889-01), the National Science Foundation (Grant No. CHE 1352608). MKG acknowledges funding from the National Institute of General Medical Sciences (2R01GM061300 and 1U01GM111528). AR and JDC are grateful to OpenEye Scientific for providing a free academic software license for use in this work. We thank four anonymous reviewers, whose comments helped us improve the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author contributions

Conceptualization, AR, JDC, DLM; Methodology, AR, JDC, DLM; Software, AR; Formal Analysis, AR, JDC; Investigation, AR, QY, SM, MS, JNM; Resources, JDC, BCG, LI, MWC, MKG, DLM; Data Curation, AR, MWC; Writing-Original Draft, AR, JDC; Writing - Review and Editing, AR, JDC, DLM, MKG, LI, BCG, SM; Visualization, AR, SM; Supervision, JDC, DLM; Project Administration, AR, JDC, DLM; Funding Acquisition, JDC, DLM, MKG, BCG, LI.

Compliance with ethical standards

Conflict of interest

JDC was a member of the Scientific Advisory Board for Schrödinger, LLC during part of this study. JDC and DLM are current members of the Scientific Advisory Board of OpenEye Scientific Software. The Chodera laboratory receives or has received funding from multiple sources, including the National Institutes of Health, the National Science Foundation, the Parker Institute for Cancer Immunotherapy, Relay Therapeutics, Entasis Therapeutics, Silicon Therapeutics, EMD Serono (Merck KGaA), AstraZeneca, the Molecular Sciences Software Institute, the Starr Cancer Consortium, Cycle for Survival, a Louis V. Gerstner Young Investigator Award, and the Sloan Kettering Institute. A complete funding history for the Chodera lab can be found at MKG has an equity interest in and is a cofounder and scientific advisor of VeraChem LLC.

Supplementary material

10822_2018_170_MOESM1_ESM.pdf (836 kb)
Supplementary material 1 (PDF 837 KB)


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Computational and Systems Biology Program, Sloan Kettering InstituteMemorial Sloan Kettering Cancer CenterNew YorkUSA
  2. 2.Tri-Institutional Training Program in Computational Biology and MedicineNew YorkUSA
  3. 3.Department of Chemistry and BiochemistryUniversity of MarylandCollege ParkUSA
  4. 4.Department of ChemistryTulane UniversityLouisianaUSA
  5. 5.Skaggs School of Pharmacy and Pharmaceutical SciencesUniversity of CaliforniaSan Diego, La JollaUSA
  6. 6.Qualcomm Institute, University of CaliforniaSan Diego, La JollaUSA
  7. 7.Department of Pharmaceutical Sciences and Department of ChemistryUniversity of CaliforniaIrvineUSA

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