New Results
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
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
John D. Chodera
1Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
Posted October 27, 2020.
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
Subject Area
Subject Areas
- Biochemistry (4990)
- Bioengineering (3492)
- Bioinformatics (15264)
- Biophysics (6922)
- Cancer Biology (5415)
- Cell Biology (7762)
- Clinical Trials (138)
- Developmental Biology (4551)
- Ecology (7175)
- Epidemiology (2059)
- Evolutionary Biology (10252)
- Genetics (7527)
- Genomics (9818)
- Immunology (4884)
- Microbiology (13278)
- Molecular Biology (5159)
- Neuroscience (29538)
- Paleontology (203)
- Pathology (840)
- Pharmacology and Toxicology (1469)
- Physiology (2149)
- Plant Biology (4772)
- Synthetic Biology (1340)
- Systems Biology (4017)
- Zoology (770)