John D. Chodera
Molecular Biology · 2007–2023
A researcher in the Science corpus: 15 papers; trusts 14 researchers and is trusted by 31 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 177 citations into it; #1,077 of 17,773 by trust here, #2,599 by citations
- Materials Chemistry — 94 citations into it; #7,668 of 11,513 by trust here, #9,387 by citations
- Atomic and Molecular Physics, and Optics — 72 citations into it; #415 of 7,135 by trust here, #873 by citations
- Spectroscopy — 38 citations into it; #416 of 1,674 by trust here, #128 by citations
- Artificial Intelligence — 15 citations into it
- Hardware and Architecture — 10 citations into it
- AI for Physical Sciences — 9 citations into it; #243 of 247 by trust here, #139 by citations
- Computer Networks and Communications — 7 citations into it
Whom John D. Chodera trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Vijay S. Pande
- Michael R. Shirts
- G. Pérez-Hernández
- Gregory R. Bowman
- Robert B. Best
- Berk Hess
- Christof Schutte
- Kyle A. Beauchamp
- Frank No'e
- David L. Mobley
Who trusts John D. Chodera overall
- Frank No'e
- David L. Mobley
- Hao Wu
- Justin A. Lemkul
- Jason Tyler Rolfe
- Theofanis Karaletsos
- Wenbo Yu
- Wei Wang
- Garrett B. Goh
- Andreas Mardt
Papers from John D. Chodera's perspective
Ordered by how strongly the researchers John D. Chodera trusts endorse each paper. Same corpus, a different order for every researcher.
- Enhanced modeling via network theory: Adaptive sampling of Markov state models
- Essential dynamics of proteins
- Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling
- Anton, a special-purpose machine for molecular dynamics simulation
- Free Energy Computations: A Mathematical Perspective
- GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers
- Intrinsic dynamics of an enzyme underlies catalysis (Nature)
- Efficient estimation of free energy differences from Monte Carlo data
- Everything you wanted to know about Markov State Models but were afraid to ask (Methods)
- Progress and challenges in the automated construction of Markov state models for full protein systems. (Journal of Chemical Physics)
Their papers
- OpenMM 7: Rapid development of high performance algorithms for molecular dynamics (2016) — 2,485 citations
- Markov models of molecular kinetics: generation and validation. (2011) — 1,235 citations
- Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics. (2007) — 597 citations
- Nonequilibrium candidate Monte Carlo is an efficient tool for equilibrium simulation (2011) — 104 citations
- Statistically optimal analysis of samples from multiple equilibrium states. (2008) — 1,757 citations
- Alchemical free energy methods for drug discovery: Progress and challenges (2011) — 538 citations
- Markov state models of biomolecular conformational dynamics (2014) — 725 citations
- Use of the Weighted Histogram Analysis Method for the Analysis of Simulated and Parallel Tempering Simulations. (2007) — 451 citations
- CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding (2021) — 83 citations
- Ensemble Docking in Drug Discovery. (2018) — 412 citations
- OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials (2023) — 337 citations
- Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.2] (2021)
- Best Practices for Constructing, Preparing, and Evaluating Protein-Ligand Binding Affinity Benchmarks [Article v1.0] (2021) — 73 citations
- OpenMM 4: A Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simulation (2012) — 656 citations
- Current Status of the AMOEBA Polarizable Force Field (2010) — 1,280 citations