Chodera Lab // MSKCC
417 East 68th Street
New York, NY 10065
Office: Z-690
Postal Address:
John ChoderaMemorial Sloan Kettering Cancer Center
1275 York Ave., ZRC 6-south
New York, NY 10065
The Chodera lab uses computation and experiments to develop quantitative, multiscale models of the effects of small molecules on biomolecular macromolecules and cellular pathways. To do this, the group utilizes physical models and rigorous statistical mechanics, with the overall goals of engineering novel therapeutics and tools for chemical biology, as well as understanding the physical driving forces behind ligand recognition the evolution of resistance mutations.
The Chodera group makes use of advanced algorithms for molecular dynamics simulations on graphics processing units (GPUs) and distributed computing platforms, in addition to robot-driven moderate and high-throughput experiments focusing on characterizing biophysical interactions between proteins and small molecules.
The lab is a core member of the AI-driven Structure-enabled Antiviral Discovery Platform (ASAP), the Folding@home Consortium, the Open Force Field Initiative, and Open Free Energy.
latest posts
Recent Publications
- A computational community blind challenge on pan-coronavirus drug discovery data2026Github: https://github.com/asapdiscovery/asap-blind-challenge-paper
- Trastuzumab Deruxtecan Resistance via Loss of HER2 Expression and BindingCancer Discovery, 2026
- Developing and benchmarking Sage 2.3.0 with the AshGC neural network charge model2025GitHub: https://github.com/openforcefield/ashgc-v1.0-fit
- Large-scale collaborative assessment of binding free energy calculations for drug discovery using OpenFE2025GitHub: https://github.com/OpenFreeEnergy/IndustryBenchmarks2024
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- The journey of data: Lessons learned in modeling kinase affinity, selectivity, and resistance [Article v1.0]Living Journal of Computational Molecular Science, 2025GitHub: https://github.com/openkinome/kinoml-techpaper-LiveCoMS
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- A structure-based computational pipeline for broad-spectrum antiviral discovery2025GitHub: https://github.com/choderalab/drugforge
- Open-science discovery of DNDI-6510, a compound that addresses genotoxic and metabolic liabilities of the COVID Moonshot SARS-CoV-2 Mpro lead inhibitor2025
- Neural network potentials for enabling advanced samll-molecule drug discovery and generative designGEN Biotechnology, 2024
- Prospective evaluation of structure-based simulations reveal their ability to predict the impact of kinase mutations on inhibitor bindingThe Journal of Physical Chemistry B, 2025GitHub: https://github.com/openkinome/study-abl-resistance
- The Need for Continuing Blinded Pose- and Activity Prediction BenchmarksThe Journal of Chemical Information and Modeling, 2025
- Fine-tuning molecular mechanics force fields to experimental free energy measurements2025GitHub: https://github.com/choderalab/timemachine_hydration_paper
- EspalomaCharge: Machine learning-enabled ultrafast partial charge assignmentJournal of Chemical Physics, 2024GitHub: https://github.com/choderalab/espaloma_charge
- DrugGym: A testbed for the economics of autonomous drug discovery2024GitHub: https://github.com/choderalab/drug-gym/
- Enhancing protein–ligand binding affinity predictions using neural network potentialsJournal of Chemical Information and Modeling, 2024GitHub: https://github.com/compsciencelab/ATM_benchmark/tree/main/ATM_With_NNPs
- Benchmarking Cross-Docking Strategies in Kinase Drug DiscoveryJournal of Chemical Information and Modeling, 2024GitHub: https://github.com/openkinome/kinase-docking-benchmark
- Nutmeg and SPICE: Models and data for biomolecular machine learningJournal of Chemical Theory and Computation, 2024GitHub: https://github.com/openmm/spice-dataset
- Machine-learned molecular mechanics force fields from large-scale quantum chemical dataChemical Science, 2024GitHub: https://github.com/choderalab/espaloma-0.3.0-manuscript
