John D. Chodera, Ph.D.
Affiliations: | 1999-2006 | Graduate Group in Biophysics | University of California, San Francisco, San Francisco, CA |
| 2007-2008 | Chemistry | Stanford University, Palo Alto, CA | |
| 2008-2012 | QB3 | University of California, Berkeley, Berkeley, CA, United States | |
| 2012- | Computational and Systems Biology Program | Memorial Sloan Kettering Cancer Center, Rockville Centre, NY, United States |
Area:
drug discovery, computational biophysics, protein folding and stabilityWebsite:
http://choderalab.orgGoogle:
"John Chodera"Mean distance: 9.43 | S | N | B | C | P |
Parents
Sign in to add mentor| William C. Swope | research assistant | 2005-2005 | IBM Research - Almaden | |
| (IBM Research Predoctoral Fellowship) | ||||
| Ken A. Dill | grad student | 2006 | UCSF | |
| (Master equation models of macromolecular dynamics from atomistic simulation.) | ||||
| Vijay S. Pande | post-doc | 2007-2008 | Stanford | |
| Phillip L. Geissler | post-doc | 2008-2012 | UC Berkeley | |
Children
Sign in to add trainee| Hersh V Gupta | research assistant | 2017-2020 | Memorial Sloan Kettering Cancer Center |
| Kendall Lemons | grad student | ||
| Alexander Matthew Payne | grad student | 2020- | Memorial Sloan Kettering Cancer Center (Microtree) |
| Mia A. Rosenfeld | post-doc | ||
| Sukrit Singh | post-doc | Memorial Sloan Kettering Cancer Center |
BETA: Related publications
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Publications
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| de Castro RL, Rodríguez-Guerra J, Schaller D, et al. (2024) Lessons learned during the journey of data: from experiment to model for predicting kinase affinity, selectivity, polypharmacology, and resistance. Biorxiv : the Preprint Server For Biology |
| Takaba K, Friedman AJ, Cavender CE, et al. (2024) Machine-learned molecular mechanics force fields from large-scale quantum chemical data. Chemical Science. 15: 12861-12878 |
| Zhang I, Rufa DA, Pulido I, et al. (2024) Correction to Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex. Journal of Chemical Theory and Computation |
| Eastman P, Galvelis R, Peláez RP, et al. (2023) OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials. The Journal of Physical Chemistry. B |
| Outhwaite IR, Singh S, Berger BT, et al. (2023) Death by a thousand cuts through kinase inhibitor combinations that maximize selectivity and enable rational multitargeting. Elife. 12 |
| Eastman P, Galvelis R, Peláez RP, et al. (2023) OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials. Arxiv |
| Boby ML, Fearon D, Ferla M, et al. (2023) Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors. Science (New York, N.Y.). 382: eabo7201 |
| Galvelis R, Varela-Rial A, Doerr S, et al. (2023) NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics. Journal of Chemical Information and Modeling. 63: 5701-5708 |
| Zhang I, Rufa DA, Pulido I, et al. (2023) Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex. Journal of Chemical Theory and Computation |
| Boothroyd S, Behara PK, Madin OC, et al. (2023) Development and Benchmarking of Open Force Field 2.0.0: The Sage Small Molecule Force Field. Journal of Chemical Theory and Computation |