John Chodera is an experienced computational chemist, and an Associate Member at Sloan Kettering Institute for Cancer Research (MSKKC). His research combines the disciplines of statistical mechanics, biomolecular simulation, and biophysical measurements to develop quantitative models for predicting and understanding how small molecules selectively bind biomolecular targets, how binding modulates conformation and function, and how mutations can perturb drug binding affinities to cause drug resistance.

John has authored over 75 articles in peer-reviewed journals, which have collectively received over 12,500 literature citations. John has also received numerous awards including the BIH Einstein Visiting Fellowship, Silicon Therapeutics Open Science Fellowship, Louis V. Gerstner Young Investigator Award, QB3-Berkeley Distinguished Postdoctoral Fellowship, IBM Predoctoral Fellowship, the Frank M. Goyan Award for outstanding work in Physical Chemistry at UCSF, and a HHMI Predoctoral Fellowship.

John holds a B.S. in Biology from Caltech and a Ph.D. in Biophysics from the University of California, San Francisco. He completed postdoctoral studies at Stanford University and at University of California, Berkeley as a QB3 Fellow.

John Chodera's research focuses on reimagining the way we develop small molecule drugs and pair therapeutics with individual patient tumors by bringing physical modeling and structure-informed machine learning into the cancer genomics era. By combining novel algorithmic advances to achieve orders-of-magnitude efficiency gains with powerful but inexpensive GPU hardware, machine learning, and distributed computing technologies, the Chodera lab is developing next-generation approaches and open source software for predicting small molecule binding affinities, designing small molecules with desired properties, predicting drug sensitivity or resistance of clinical mutations, and understanding the detailed structural mechanisms underlying oncogenic mutations. The Chodera lab co-develops the OpenMM GPU-powered molecular simulation framework, which powers numerous biomolecular modeling and simulation applications using physical modeling and machine learning. As a core member of the Folding@home Consortium, the lab harnesses the largest computing platform in the world—the first to reach an exaFLOP/s—pooling the efforts of a million volunteers around the world to study functional implications of mutations and new opportunities for therapeutic design against cancer targets and global pandemics. Dr. Chodera co-founded the Open Force Field Initiative, a scientific collaboration funded by the NIH and an industry consortium consisting of dozens of scientists working to develop modern open source infrastructure for building and applying high-quality biomolecular force fields. Dr. Chodera is a co-founder of the COVID Moonshot, a radical open science patent-free drug discovery effort aiming to develop an inexpensive small molecule therapy effective against COVID-19 and future coronavirus. Using automated biophysical measurements, the Chodera laboratory collects new experimental data targeted to advance the quantitative accuracy of our methodologies, and gather new insight into drug susceptibility and resistance in kinases and other cancer targets. Their work makes extensive use of scalable Bayesian statistical inference, machine learning via probabilistic programming, and information theoretic principles for designing experiments and quantifying error. Dr. Chodera is passionate about open science, disseminating scientific best practices, and maximizing research reproducibility.