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COSMO-RS blind prediction of distribution coefficients and aqueous pKa values from the SAMPL8 challenge

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Abstract

The SAMPL8 blind prediction challenge, which addresses the acid/base dissociation constants (pKa) and the distribution coefficients (logD), was addressed by the Conductor like Screening Model for Realistic Solvation (COSMO-RS). Using the COSMOtherm implementation of COSMO-RS together with a rigorous conformational sampling, yielded logD predictions with a root mean square deviation (RMSD) of 1.36 log units over all 11 compounds and seven bi-phasic systems of the data set, which was the most accurate of all contest submissions (logD).

For the SAMPL8 pKa competition, participants were asked to report the standard state free energies of all microstates, which were then used to calculate the macroscopic pKa. We have used COSMO-RS based linear free energy fit models to calculate the requested energies. The assignment of the calculated and experimental pKa values was made on the basis of the popular transitions, i.e. the transition hat was predicted by the majority of the submissions. With this assignment and a model that covers both, pKa and base pKa, we achieved an RMSD of 3.44 log units (18 pKa values of 14 molecules), which is the second place of the six ranked submissions. By changing to an assignment that is based on the experimental transition curves, the RMSD reduces to 1.65. In addition to the ranked contribution, we submitted two more data sets, one for the standard pKa model and one or the standard base pKa model of COSMOtherm. Using the experiment based assignment with the predictions of the two sets we received a RMSD of 1.42 log units (25 pKa values of 20 molecules). The deviation mainly arises from a single outlier compound, the omission of which leads to an RMSD of 0.89 log units.

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Data Availability

All data generated or analyzed during this study are available in this published article, its supplementary information files, or reference [30].

Notes

  1. In cases where the full protonation range could be described by the COSMOtherm pKa (SAMPL8-2) or the base pKa model (SAMPL8-10, SAMPL8-11, SAMPL8-15, SAMPL8-8), these models were used instead of the unified model.

Abbreviations

COSMO-RS:

Conductor like Screening Model for Realistic Solvation

COSMO:

Conductor like Screening Model

DMF:

Dimethylformamide

LFER:

Linear free energy relationship

MAD:

Mean absolute deviation between predicted and experimental data

MEK:

Methyl ethyl ketone

RMSD:

Root mean square deviation between predicted and experimental data

TBME:

Tert butyl methyl ether

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Acknowledgements

We thank the organizers for setting up the SAMPL8 challenge and the National Institutes of Health for its support of the SAMPL project via R01GM124270 to David L. Mobley (UC Irvine). We appreciate Juliana Gretz, Paul Czodrowski, Nicolas Tielker, and Stefan M. Kast of the TU Dortmund for sharing their acidity constants. MD thanks David L. Mobley and Aakankschit Nandkeolyar for fruitful discussions.

Funding

This research was funded solely by Dassault Systèmes.

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Authors and Affiliations

  1. Dassault Systèmes Deutschland GmbH, Am Kabellager 11-13, 51063, Cologne, Germany

    Michael Diedenhofen, Frank Eckert & Selman Terzi

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  1. Michael Diedenhofen
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Contributions

MD and FE wrote the manuscript. All authors reviewed the manuscript and approved the version to be published. FE and MD analyzed the data and interpreted the results. ST fitted the parameters of the unified pKa LFER model. MD ran the tautomer/conformer generation and performed the COSMO-RS predictions.

Corresponding author

Correspondence to Michael Diedenhofen.

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The authors declare the following competing financial interests: MD, FE, and ST are employees of Dassault Systèmes, BIOVIA. Dassault Systèmes commercially distributes the software COSMOtherm, COSMOconf, COSMOquick, COSMObase, and TURBOMOLE, which were used in the present study.

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Diedenhofen, M., Eckert, F. & Terzi, S. COSMO-RS blind prediction of distribution coefficients and aqueous pKa values from the SAMPL8 challenge. J Comput Aided Mol Des 37, 395–405 (2023). https://doi.org/10.1007/s10822-023-00514-4

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