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Fitting quantum machine learning potentials to experimental free energy data: Predicting tautomer ratios in solution

View ORCID ProfileMarcus Wieder, Josh Fass, View ORCID ProfileJohn D. Chodera
doi: https://doi.org/10.1101/2020.10.24.353318
Marcus Wieder
1Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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  • For correspondence: marcus.wieder@choderalab.org
Josh Fass
1Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
2Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Graduate School of Medical Sciences, New York, NY 10065, USA
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John D. Chodera
1Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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Abstract

The computation of tautomer rations of druglike molecules is enormously important in computer-aided drug discovery, as over a quarter of all approved drugs can populate multiple tautomeric species in solution. Unfortunately, accurate calculations of aqueous tautomer ratios—the degree to which these species must be penalized in order to correctly account for tautomers in modeling binding for computer-aided drug discovery—is surprisingly difficult. While quantum chemical approaches to computing aqueous tautomer ratios using continuum solvent models and rigid-rotor harmonic-oscillator thermochemistry are currently state of the art, these methods are still surprisingly inaccurate despite their enormous computational expense. Here, we show that a major source of this inaccuracy lies in the breakdown of the standard approach to accounting for quantum chemical thermochemistry using rigid rotor harmonic oscillator (RRHO) approximations, which are frustrated by the complex conformational landscape introduced by the migration of double bonds, creation of stereocenters, and introduction of multiple conformations separated by low energetic barriers induced by migration of a single proton. Using quantum machine learning (QML) methods that allow us to compute potential energies with quantum chemical accuracy at a fraction of the cost, we show how rigorous relative alchemical free energy calculations can be used to compute tautomer ratios in vacuum free from the limitations introduced by RRHO approximations. Furthermore, since the parameters of QML methods are tunable, we show how we can train these models to correct limitations in the underlying learned quantum chemical potential energy surface using free energies, enabling these methods to learn to generalize tautomer free energies across a broader range of predictions.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Results section notation clarified, updated title of Figure 4 and updated error bars on Figure 8. Notation of free energy terms clarified.

  • https://github.com/choderalab/neutromeratio

  • ↵1 https://aiqm.github.io/torchani/examples/nnp_training.html

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 27, 2020.
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Fitting quantum machine learning potentials to experimental free energy data: Predicting tautomer ratios in solution
Marcus Wieder, Josh Fass, John D. Chodera
bioRxiv 2020.10.24.353318; doi: https://doi.org/10.1101/2020.10.24.353318
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Fitting quantum machine learning potentials to experimental free energy data: Predicting tautomer ratios in solution
Marcus Wieder, Josh Fass, John D. Chodera
bioRxiv 2020.10.24.353318; doi: https://doi.org/10.1101/2020.10.24.353318

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