John D. Chodera
Papers
3
Total Citations
29
H-Index
2
About
John D. Chodera is a leading computational biophysicist whose work centers on advancing predictive modeling for biomolecular design, with a particular focus on free energy methods and the rigorous treatment of experimental data. His major contributions include pioneering the use of the bootstrap principle to model error in experimental assays, a technique that allows researchers to simulate and propagate sources of bias and uncertainty into assay results—crucial for reconciling discrepancies between different dispensing technologies. This work, detailed in his 2015 paper (17 citations), provides a statistical framework to improve the reliability of high-throughput screening data. Chodera is also a driving force behind community blind prediction challenges, as outlined in his 2016 work (10 citations), which aim to accelerate innovation in predicting biomolecular interactions and binding free energies. By focusing on model systems to benchmark and refine computational methods, he has helped position free energy methods as transformative tools for drug discovery. His research emphasizes transparency and reproducibility, making him a key figure in the push toward more robust quantitative biomolecular science.
Research Focus
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