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Overview of the SAMPL6 pKa challenge: evaluating small molecule microscopic and macroscopic pKa predictions

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Abstract

The prediction of acid dissociation constants (pKa) is a prerequisite for predicting many other properties of a small molecule, such as its protein–ligand binding affinity, distribution coefficient (log D), membrane permeability, and solubility. The prediction of each of these properties requires knowledge of the relevant protonation states and solution free energy penalties of each state. The SAMPL6 pKa Challenge was the first time that a separate challenge was conducted for evaluating pKa predictions as part of the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) exercises. This challenge was motivated by significant inaccuracies observed in prior physical property prediction challenges, such as the SAMPL5 log D Challenge, caused by protonation state and pKa prediction issues. The goal of the pKa challenge was to assess the performance of contemporary pKa prediction methods for drug-like molecules. The challenge set was composed of 24 small molecules that resembled fragments of kinase inhibitors, a number of which were multiprotic. Eleven research groups contributed blind predictions for a total of 37 pKa distinct prediction methods. In addition to blinded submissions, four widely used pKa prediction methods were included in the analysis as reference methods. Collecting both microscopic and macroscopic pKa predictions allowed in-depth evaluation of pKa prediction performance. This article highlights deficiencies of typical pKa prediction evaluation approaches when the distinction between microscopic and macroscopic pKas is ignored; in particular, we suggest more stringent evaluation criteria for microscopic and macroscopic pKa predictions guided by the available experimental data. Top-performing submissions for macroscopic pKa predictions achieved RMSE of 0.7–1.0 pKa units and included both quantum chemical and empirical approaches, where the total number of extra or missing macroscopic pKas predicted by these submissions were fewer than 8 for 24 molecules. A large number of submissions had RMSE spanning 1–3 pKa units. Molecules with sulfur-containing heterocycles or iodo and bromo groups were less accurately predicted on average considering all methods evaluated. For a subset of molecules, we utilized experimentally-determined microstates based on NMR to evaluate the dominant tautomer predictions for each macroscopic state. Prediction of dominant tautomers was a major source of error for microscopic pKa predictions, especially errors in charged tautomers. The degree of inaccuracy in pKa predictions observed in this challenge is detrimental to the protein-ligand binding affinity predictions due to errors in dominant protonation state predictions and the calculation of free energy corrections for multiple protonation states. Underestimation of ligand pKa by 1 unit can lead to errors in binding free energy errors up to 1.2 kcal/mol. The SAMPL6 pKa Challenge demonstrated the need for improving pKa prediction methods for drug-like molecules, especially for challenging moieties and multiprotic molecules.

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Data availability

SAMPL6 \(\text {p}K_{\text{a}}\) challenge instructions, submissions, experimental data and analysis is available at SAMPL6 GitHub Repository: https://github.com/samplchallenges/SAMPL6. An archive copy of the pKa Challenge directory of SAMPL6 GitHub Repository (SAMPL6-repository-pKadirectory.zip) is also available in the Supplementary Documents bundle (Electronic Supplementary Material 2). Supplementary Documents bundle also includes the following: (1) Table S1 in CSV format (SAMPL6-pKa-chemical-identiers-table.csv), (2) Table S2 in CSV format (macroscopic-pKa-statistics-24mol-hungarian-match.csv), (3) Table S3 in CSV format (microscopic-pKa-statistics-8mol-hungarian-match-table.csv), (4) Table S4 in CSV format (microscopic-pKa-statistics-8mol-microstate- match-table.csv), (5) Figure S1 in CSV format (experimental-microstates-of-8mol-based-on-NMR.csv), (6) The JupyterNotebook used for the enumeration of microstates (enumerate-microstates-with-Epik-and-OpenEye-QUACPAC.ipynb), (7) A CSV table of SAMPL6 molecule IDs and OpenEye OEChem generated SMILES (molecule_ID_and_SMILES.csv).

Abbreviations

SAMPL:

Statistical Assessment of the Modeling of Proteins and Ligands

pK a :

\(-\log _{10}\) of the acid dissociation equilibrium constant

log P :

\(\log _{10}\) of the organic solvent-water partition coefficient (\(K_{ow}\)) of neutral species

log D :

\(\log _{10}\) of organic solvent-water distribution coefficient (\(D_{ow}\))

SEM:

Standard error of the mean

RMSE:

Root mean squared error

MAE:

Mean absolute error

\(\tau \) :

Kendall’s rank correlation coefficient (Tau)

R2 :

Coefficient of determination (R-Squared)

MPSC:

Multiple protonation states correction for binding free energy

DL:

Database lookup

LFER:

Linear free energy relationship

QSPR:

Quantitative structure–property relationship

ML:

Machine learning

QM:

Quantum mechanics

LEC:

Linear empirical correction

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Acknowledgements

We would like to acknowledge the infrastructure and website support of Mike Chiu that allowed a seamless collection of challenge submissions. Mike Chiu also provided assistance with constructing a submission validation script to ensure all submissions adhered to the machine-readable format. We are grateful to Kiril Lanevskij for suggesting the Hungarian algorithm for matching experimental and predicted \(\text {p}K_{\text{a}}\) values. We would like to thank Thomas Fox for providing MoKa reference calculations. We acknowledge Caitlin Bannan for guidance on defining a working microstate definition for the challenge and guidance for designing the challenge. We thank Brad Sherborne for his valuable insights at the conception of the \(\text {p}K_{\text{a}}\) challenge and connecting us with Timothy Rhodes and Dorothy Levorse who were able to provide resources and expertise for experimental measurements performed at MRL. We acknowledge Paul Czodrowski who provided feedback on multiple stages of this work: challenge construction, purchasable compound selection, and manuscript draft. MI, JDC, and DLM gratefully acknowledge support from NIH Grant R01GM124270 supporting the SAMPL Blind Challenges. MI, ASR, AR, and JDC acknowledge support from the Sloan Kettering Institute. JDC acknowledges support from NIH Grant P30CA008748 and NIH Grant R01GM121505. DLM appreciates financial support from the National Institutes of Health (Grant No. R01GM108889) and the National Science Foundation (Grant No. CHE 1352608). MRG acknowledges support of MCB-1519640 from the National Science Foundation. MI acknowledges Doris J. Hutchinson Fellowship. MI, ASR, AR, and JDC are grateful to OpenEye Scientific for providing a free academic software license for use in this work. MI, ASR, AR, and JDC thank Janos Fejervari and ChemAxon team that gave us permission to include ChemAxon/Chemicalize \(\text {p}K_{\text{a}}\) predictions as a reference prediction in challenge analysis.

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Authors and Affiliations

  1. Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA

    Mehtap Işık, Ariën S. Rustenburg, Andrea Rizzi & John D. Chodera

  2. Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY, 10065, USA

    Mehtap Işık

  3. Graduate Program in Physiology, Biophysics, and Systems Biology, Weill Cornell Medical College, New York, NY, 10065, USA

    Ariën S. Rustenburg

  4. Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY, 10065, USA

    Andrea Rizzi

  5. Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, Irvine, CA, 92697, USA

    David L. Mobley

  6. Department of Physics, City College of New York, New York, NY, 10031, USA

    M. R. Gunner

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  1. Mehtap Işık
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Contributions

Conceptualization, MI, JDC ; Methodology, MI, JDC, ASR ; Software, MI, AR, ASR ; Formal Analysis, MI, ASR ; Investigation, MI ; Resources, JDC, DLM; Data Curation, MI ; Writing-Original Draft, MI; Writing - Review and Editing, MI, JDC, ASR, AR, DLM, MRG; Visualization, MI, AR ; Supervision, JDC, DLM ; Project Administration, MI ; Funding Acquisition, JDC, DLM, MI.

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Conflict of interest

JDC was a member of the Scientific Advisory Board for Schrödinger, LLC during part of this study, and is a current Scientific Advisory Board member for OpenEye Scientific and scientific advisor to Foresite Labs. DLM is a current member of the Scientific Advisory Board of OpenEye Scientific and an Open Science Fellow with Silicon Therapeutics. 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, Vir Biotechnology, Silicon Therapeutics, EMD Serono (Merck KGaA), AstraZeneca, Vir Biotechnology, XtalPi, the Molecular Sciences Software Institute, the Starr Cancer Consortium, the Open Force Field Consortium, Cycle for Survival, a Louis V. Gerstner Young Investigator Award, The Einstein Foundation, and the Sloan Kettering Institute. A complete list of funding can be found at http://choderalab.org/funding.

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Işık, M., Rustenburg, A.S., Rizzi, A. et al. Overview of the SAMPL6 pKa challenge: evaluating small molecule microscopic and macroscopic pKa predictions. J Comput Aided Mol Des 35, 131–166 (2021). https://doi.org/10.1007/s10822-020-00362-6

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