John D. Chodera
computational biologist
Facts
9 properties- Instance of
- human
- Occupation
- computational biologist
- Employer
- Memorial Sloan Kettering Cancer Center (2012–2019), University of California, Berkeley (2008–2012), Stanford University (2007–2008)[2][3][4]
- Educated at
- University of California, San Francisco (?–2006)[5]
- Doctoral advisor
- Ken A. Dill[6]
- Imported from
- wholeness_audit_2026-05-02
🏷️ Also known as
en John Chodera
en John Damon Chodera
en John D. Chodera
es John D Chodera
🔖 Identifiers · 10
- ORCID iD
- 0000-0003-0542-119X
- OpenAlex ID
- A5083925073, A5123748574, A5124072777, A5125130194, A5126957245, A5134830647, A5136700046, A5136946700, A5137263929, A5137674596as of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
- Academic Tree ID
- 146103
- DBLP author ID
- 52/3000
- GitHub account
- choderalab
- Google Scholar author ID
- nnEg7_8AAAAJ
- Mastodon address
- johnchodera@fediscience.org (2022–present)
- Mathematics Genealogy Project ID
- 262796
- Scopus author ID
- 6506773320
- X (Twitter) username
- jchodera (2008–present)
📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Author of 17
Advancing Predictive Modeling Through Focused Development Of Model Systems To Drive New Modeling Innovations,
Bayesian-Inference-Driven Model Parametrization and Model Selection for 2CLJQ Fluid Models,
Binding Thermodynamics of Host–Guest Systems with SMIRNOFF99Frosst 1.0.5 from the Open Force Field Initiative,
Development and Benchmarking of Open Force Field 2.0.0: The Sage Small Molecule Force Field,
End-to-end differentiable construction of molecular mechanics force fields,
Enhancing Protein–Ligand Binding Affinity Predictions Using Neural Network Potentials,
Fitting quantum machine learning potentials to experimental free energy data: predicting tautomer ratios in solution,
Machine-learned molecular mechanics force fields from large-scale quantum chemical data
+9 more
← Authored by 17
Advancing Predictive Modeling Through Focused Development Of Model Systems To Drive New Modeling Innovations,
Bayesian-Inference-Driven Model Parametrization and Model Selection for 2CLJQ Fluid Models,
Binding Thermodynamics of Host–Guest Systems with SMIRNOFF99Frosst 1.0.5 from the Open Force Field Initiative,
Development and Benchmarking of Open Force Field 2.0.0: The Sage Small Molecule Force Field,
End-to-end differentiable construction of molecular mechanics force fields,
Enhancing Protein–Ligand Binding Affinity Predictions Using Neural Network Potentials,
Fitting quantum machine learning potentials to experimental free energy data: predicting tautomer ratios in solution,
Machine-learned molecular mechanics force fields from large-scale quantum chemical data
+9 more
Affiliated with 2
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Summary
John D. Chodera worked as a computational biologist[1].
Body
Career and Affiliations
Employers include Memorial Sloan Kettering Cancer Center[2], a hospital[14], in United States[15], founded in 1884[16], headquartered in New York City[17]; University of California, Berkeley[3], a public research university[18], in United States[19], founded in 1868[20], headquartered in Berkeley[21]; and Stanford University[4], a private university[22], in United States[23], founded in 1885[24], headquartered in Stanford[25].
FAQs
What did John D. Chodera do for work?
John D. Chodera worked as computational biologist[1].
Where did John D. Chodera go to school?
John D. Chodera was educated at University of California, San Francisco[5].