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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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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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