John D. Chodera

According to our database, John D. Chodera authored at least 32 papers between 2003 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Enhancing Protein-Ligand Binding Affinity Predictions Using Neural Network Potentials.

Francesc Sabanés Zariquiey, Raimondas Galvelis, Emilio Gallicchio, John D. Chodera, Thomas E. Markland, Gianni De Fabritiis

J. Chem. Inf. Model., March, 2024

2023

NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics.

Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr, Roberto Fino, Peter K. Eastman, Thomas E. Markland, John D. Chodera, Gianni De Fabritiis

J. Chem. Inf. Model., September, 2023

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.

Peter K. Eastman, Raimondas Galvelis, Raúl P. Peláez, Charlles R. A. Abreu, Stephen E. Farr, Emilio Gallicchio, Anton Gorenko, Michael M. Henry, Frank Hu, Jing Huang, Andreas Krämer, Julien Michel, Joshua A. Mitchell, Vijay S. Pande, João P. G. L. M. Rodrigues, Jaime Rodríguez-Guerra, Andrew C. Simmonett, Jason M. Swails, Ivy Zhang, John D. Chodera, Gianni De Fabritiis, Thomas E. Markland

CoRR, 2023

Espaloma-0.3.0: Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond.

Kenichiro Takaba, Iván Pulido, Mike Henry, Hugo MacDermott-Opeskin, John D. Chodera, Yuanqing Wang

CoRR, 2023

EspalomaCharge: Machine learning-enabled ultra-fast partial charge assignment.

Yuanqing Wang, Iván Pulido, Kenichiro Takaba, Benjamin Kaminow, Jenke Scheen, Lily Wang, John D. Chodera

CoRR, 2023

Spatial Attention Kinetic Networks with E(n)-Equivariance.

Yuanqing Wang, John D. Chodera

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Bayesian-Inference-Driven Model Parametrization and Model Selection for 2CLJQ Fluid Models.

Owen Madin, Simon Boothroyd, Richard A. Messerly, Josh Fass, John D. Chodera, Michael R. Shirts

J. Chem. Inf. Model., 2022

Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization at Scale.

Joshua T. Horton, Simon Boothroyd, Jeffrey Wagner, Joshua A. Mitchell, Trevor Gokey, David L. Dotson, Pavan Kumar Behara, Venkata Krishnan Ramaswamy, Mark Mackey, John D. Chodera, Jamshed Anwar, David L. Mobley, Daniel J. Cole

J. Chem. Inf. Model., 2022

SAMPL7 protein-ligand challenge: A community-wide evaluation of computational methods against fragment screening and pose-prediction.

Harold Grosjean, Mehtap Isik, Anthony Aimon, David L. Mobley, John D. Chodera, Frank von Delft, Philip C. Biggin

J. Comput. Aided Mol. Des., 2022

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials.

Peter K. Eastman, Pavan Kumar Behara, David L. Dotson, Raimondas Galvelis, John E. Herr, Josh T. Horton, Yuezhi Mao, John D. Chodera, Benjamin P. Pritchard, Yuanqing Wang, Gianni De Fabritiis, Thomas E. Markland

CoRR, 2022

NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics.

Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr, Roberto Fino, Peter K. Eastman, Thomas E. Markland, John D. Chodera, Gianni De Fabritiis

CoRR, 2022

2021

Overview of the SAMPL6 pK_a challenge: evaluating small molecule microscopic and macroscopic pK_a predictions.

Mehtap Isik, Ariën S. Rustenburg, Andrea Rizzi, Marilyn R. Gunner, David L. Mobley, John D. Chodera

J. Comput. Aided Mol. Des., 2021

Automated high throughput pK_a and distribution coefficient measurements of pharmaceutical compounds for the SAMPL8 blind prediction challenge.

Matthew N. Bahr, Aakankschit Nandkeolyar, John K. Kenna, Neysa Nevins, Luigi Da Vià, Mehtap Isik, John D. Chodera, David L. Mobley

J. Comput. Aided Mol. Des., 2021

2020

Is Structure-Based Drug Design Ready for Selectivity Optimization?

Steven K. Albanese, John D. Chodera, Andrea Volkamer, Simon Keng, Robert Abel, Lingle Wang

J. Chem. Inf. Model., 2020

The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations.

Andrea Rizzi, Travis Jensen, David R. Slochower, Matteo Aldeghi, Vytautas Gapsys, Dimitris Ntekoumes, Stefano Bosisio, Michail Papadourakis, Niel M. Henriksen, Bert L. de Groot, Zoe Cournia, Alex Dickson, Julien Michel, Michael K. Gilson, Michael R. Shirts, David L. Mobley, John D. Chodera

J. Comput. Aided Mol. Des., 2020

Octanol-water partition coefficient measurements for the SAMPL6 blind prediction challenge.

Mehtap Isik, Dorothy Levorse, David L. Mobley, Timothy Rhodes, John D. Chodera

J. Comput. Aided Mol. Des., 2020

Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II log P Challenge.

Mehtap Isik, Teresa Danielle Bergazin, Thomas Fox, Andrea Rizzi, John D. Chodera, David L. Mobley

J. Comput. Aided Mol. Des., 2020

Standard state free energies, not pK_as, are ideal for describing small molecule protonation and tautomeric states.

Marilyn R. Gunner, Taichi Murakami, Ariën S. Rustenburg, Mehtap Isik, John D. Chodera

J. Comput. Aided Mol. Des., 2020

End-to-End Differentiable Molecular Mechanics Force Field Construction.

Yuanqing Wang, Josh Fass, John D. Chodera

CoRR, 2020

2019

Sharing Data from Molecular Simulations.

Mark James Abraham, Rossen Apostolov, Jonathan Barnoud, Paul Bauer, Christian Blau, Alexandre M. J. J. Bonvin, Matthieu Chavent, John D. Chodera, Karmen Condic-Jurkic, Lucie Delemotte, Helmut Grubmüller, Rebecca J. Howard, E. Joseph Jordan, Erik Lindahl, Samuli Ollila, Jana Selent, Daniel G. A. Smith, Phillip J. Stansfeld, Johanna K. S. Tiemann, Mikaël Trellet, Christopher J. Woods, Artem A. Zhmurov

J. Chem. Inf. Model., 2019

Graph Nets for Partial Charge Prediction.

Yuanqing Wang, Josh Fass, Chaya D. Stern, Kun Luo, John D. Chodera

CoRR, 2019

2018

Overview of the SAMPL6 host-guest binding affinity prediction challenge.

Andrea Rizzi, Steven Murkli, John N. McNeill, Wei Yao, Matthew R. Sullivan, Michael K. Gilson, Michael Chiu, Lyle Isaacs, Bruce C. Gibb, David L. Mobley, John D. Chodera

J. Comput. Aided Mol. Des., 2018

pK a measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments.

Mehtap Isik, Dorothy Levorse, Ariën S. Rustenburg, Ikenna E. Ndukwe, Heather Wang, Xiao Wang, Mikhail Y. Reibarkh, Gary E. Martin, Alexey A. Makarov, David L. Mobley, Timothy Rhodes, John D. Chodera

J. Comput. Aided Mol. Des., 2018

Quantifying Configuration-Sampling Error in Langevin Simulations of Complex Molecular Systems.

Josh Fass, David A. Sivak, Gavin E. Crooks, Kyle Beauchamp, Benedict J. Leimkuhler, John D. Chodera

Entropy, 2018

2017

OpenMM 7: Rapid development of high performance algorithms for molecular dynamics.

Peter K. Eastman, Jason M. Swails, John D. Chodera, Robert McGibbon, Yutong Zhao, Kyle Beauchamp, Lee-Ping Wang, Andrew C. Simmonett, Matthew P. Harrigan, Chaya D. Stern, Rafal P. Wiewiora, Bernard R. Brooks, Vijay S. Pande

PLoS Comput. Biol., 2017

2016

Ensembler: Enabling High-Throughput Molecular Simulations at the Superfamily Scale.

Daniel L. Parton, Patrick B. Grinaway, Sonya M. Hanson, Kyle Beauchamp, John D. Chodera

PLoS Comput. Biol., 2016

Measuring experimental cyclohexane-water distribution coefficients for the SAMPL5 challenge.

Ariën S. Rustenburg, Justin Dancer, Baiwei Lin, Jianwen A. Feng, Daniel F. Ortwine, David L. Mobley, John D. Chodera

J. Comput. Aided Mol. Des., 2016

2015

Introduction to the special issue: Data Part 2: Experimental Data.

Christian Kramer, John D. Chodera, Terry R. Stouch

J. Comput. Aided Mol. Des., 2015

Modeling error in experimental assays using the bootstrap principle: understanding discrepancies between assays using different dispensing technologies.

Sonya M. Hanson, Sean Ekins, John D. Chodera

J. Comput. Aided Mol. Des., 2015

2013

Identifying ligand binding sites and poses using GPU-accelerated Hamiltonian replica exchange molecular dynamics.

Kai Wang, John D. Chodera, Yanzhi Yang, Michael R. Shirts

J. Comput. Aided Mol. Des., 2013

2006

Long-Time Protein Folding Dynamics from Short-Time Molecular Dynamics Simulations.

John D. Chodera, William C. Swope, Jed W. Pitera, Ken A. Dill

Multiscale Model. Simul., 2006

2003

MOPED: Method for optimizing physical energy parameters using decoys.

Chaok Seok, J. Ben Rosen, John D. Chodera, Ken A. Dill

J. Comput. Chem., 2003