Chodera lab // MSKCC
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Chodera lab // MSKCC

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src-imatinib-1040.jpg

Chodera lab // MSKCC

Changing drug discovery one ratio of partition functions at a time

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Chodera lab // MSKCC

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The Chodera lab uses computation and experiment to develop quantitative, multiscale models of the effects of small molecules on biomolecular macromolecules and cellular pathways and understand the functional and therapeutic ramifications of mutations. The group utilizes physical models, rigorous statistical mechanics, and open source software development practices with overall goals of engineering novel therapeutics and tools for chemical biology, predicting resistance or susceptibility to therapy, and understanding the physical driving forces behind the emergence of drug resistance. We develop and use advanced algorithms for molecular dynamics simulations on GPUs and distributed computing platforms, in addition to high-throughput experiments to characterize biophysical interactions between small molecules and their targets.

Our 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 the COVID Moonshot.

FEATURED RESEARCH PROJECTS

Research Projects
Rational design of small molecules
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Kinase inhibitor selectivity and design
Predicting drug susceptibility and the emergence of drug resistance
Epigenetic cancer targets
Nanoparticles for targeted drug delivery
Automated ligand design
Automated biophysical measurements to drive improvements in physical modeling accuracy
Automated biophysical measurements to drive improvements in physical modeling accuracy
Design of small molecule allosteric modulators
Bayesian inference and error modeling for experimental data
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Chodera lab // MSKCC

The Chodera lab at the Memorial Sloan-Kettering Cancer Center

RECENT PUBLICATIONS

Featured
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Nov 17, 2024
Prospective evaluation of structure-based simulations reveal their ability to predict the impact of kinase mutations on inhibitor binding
Nov 17, 2024
Nov 17, 2024
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Sep 25, 2024
Nutmeg and SPICE: Models and data for biomolecular machine learning
Sep 25, 2024
Sep 25, 2024
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Sep 10, 2024
Lessons learned during the journey of data: from experiment to model for predicting kinase affinity, selectivity, polypharmacology, and resistance
Sep 10, 2024
Sep 10, 2024
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Jun 26, 2024
Machine-learned molecular mechanics force fields from large-scale quantum chemical data
Jun 26, 2024
Jun 26, 2024

RECENT NEWS

Featured
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Dec 6, 2024
ELLIS ML4Molecules Workshop in Berlin
Dec 6, 2024
Dec 6, 2024
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Nov 3, 2024
Postdoctoral Fellow Maria A. Castellanos wins poster prize at Computational Medicinal Chemistry School for AlphaFold-based prediction of antiviral spectrum
Nov 3, 2024
Nov 3, 2024
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Oct 29, 2024
ICBS2024 International Chemical Biology Society 2025 keynote in Toronto
Oct 29, 2024
Oct 29, 2024

RECENT TWEETS