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John D. Chodera
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2020 – today
- 2024
- [j24]Francesc Sabanés Zariquiey
, Raimondas Galvelis
Enhancing Protein-Ligand Binding Affinity Predictions Using Neural Network Potentials. J. Chem. Inf. Model. 64(5): 1481-1485 (2024) - [i9]Peter K. Eastman, Benjamin P. Pritchard, John D. Chodera, Thomas E. Markland:
Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning. CoRR abs/2406.13112 (2024) - 2023
- [j23]Raimondas Galvelis
NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics. J. Chem. Inf. Model. 63(18): 5701-5708 (2023) - [c1]Yuanqing Wang, John D. Chodera:
Spatial Attention Kinetic Networks with E(n)-Equivariance. ICLR 2023 - [i8]Yuanqing Wang, John D. Chodera:
Spatial Attention Kinetic Networks with E(n)-Equivariance. CoRR abs/2301.08893 (2023) - [i7]Yuanqing Wang, Iván Pulido, Kenichiro Takaba
EspalomaCharge: Machine learning-enabled ultra-fast partial charge assignment. CoRR abs/2302.06758 (2023) - [i6]Kenichiro Takaba, Iván Pulido, Mike Henry, Hugo MacDermott-Opeskin, John D. Chodera, Yuanqing Wang:
Espaloma-0.3.0: Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond. CoRR abs/2307.07085 (2023) - [i5]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
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials. CoRR abs/2310.03121 (2023) - 2022
- [j22]Harold Grosjean
SAMPL7 protein-ligand challenge: A community-wide evaluation of computational methods against fragment screening and pose-prediction. J. Comput. Aided Mol. Des. 36(4): 291-311 (2022) - [j21]Owen Madin
Bayesian-Inference-Driven Model Parametrization and Model Selection for 2CLJQ Fluid Models. J. Chem. Inf. Model. 62(4): 874-889 (2022) - [j20]Joshua T. Horton
Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization at Scale. J. Chem. Inf. Model. 62(22): 5622-5633 (2022) - [i4]Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr, Roberto Fino, Peter K. Eastman, Thomas E. Markland, John D. Chodera, Gianni De Fabritiis:
NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics. CoRR abs/2201.08110 (2022) - [i3]Peter K. Eastman, Pavan Kumar Behara
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials. CoRR abs/2209.10702 (2022) - 2021
- [j19]Mehtap Isik
Overview of the SAMPL6 pKa challenge: evaluating small molecule microscopic and macroscopic pKa predictions. J. Comput. Aided Mol. Des. 35(2): 131-166 (2021) - [j18]Matthew N. Bahr
Automated high throughput pKa and distribution coefficient measurements of pharmaceutical compounds for the SAMPL8 blind prediction challenge. J. Comput. Aided Mol. Des. 35(11): 1141-1155 (2021) - 2020
- [j17]Mehtap Isik
Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II log P Challenge. J. Comput. Aided Mol. Des. 34(4): 335-370 (2020) - [j16]Mehtap Isik
Octanol-water partition coefficient measurements for the SAMPL6 blind prediction challenge. J. Comput. Aided Mol. Des. 34(4): 405-420 (2020) - [j15]Marilyn R. Gunner
Standard state free energies, not pKas, are ideal for describing small molecule protonation and tautomeric states. J. Comput. Aided Mol. Des. 34(5): 561-573 (2020) - [j14]Andrea Rizzi
The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations. J. Comput. Aided Mol. Des. 34(5): 601-633 (2020) - [j13]Steven K. Albanese
Is Structure-Based Drug Design Ready for Selectivity Optimization? J. Chem. Inf. Model. 60(12): 6211-6227 (2020) - [i2]Yuanqing Wang, Josh Fass, John D. Chodera:
End-to-End Differentiable Molecular Mechanics Force Field Construction. CoRR abs/2010.01196 (2020)
2010 – 2019
- 2019
- [j12]Mark James Abraham
Sharing Data from Molecular Simulations. J. Chem. Inf. Model. 59(10): 4093-4099 (2019) - [i1]Yuanqing Wang, Josh Fass, Chaya D. Stern, Kun Luo, John D. Chodera:
Graph Nets for Partial Charge Prediction. CoRR abs/1909.07903 (2019) - 2018
- [j11]Josh Fass
Quantifying Configuration-Sampling Error in Langevin Simulations of Complex Molecular Systems. Entropy 20(5): 318 (2018) - [j10]Andrea Rizzi
Overview of the SAMPL6 host-guest binding affinity prediction challenge. J. Comput. Aided Mol. Des. 32(10): 937-963 (2018) - [j9]Mehtap Isik, Dorothy Levorse, Ariën S. Rustenburg, Ikenna E. Ndukwe, Heather Wang, Xiao Wang, Mikhail Y. Reibarkh
pK a measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments. J. Comput. Aided Mol. Des. 32(10): 1117-1138 (2018) - 2017
- [j8]Peter K. Eastman
OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLoS Comput. Biol. 13(7) (2017) - 2016
- [j7]Ariën S. Rustenburg, Justin Dancer, Baiwei Lin, Jianwen A. Feng, Daniel F. Ortwine, David L. Mobley, John D. Chodera
Measuring experimental cyclohexane-water distribution coefficients for the SAMPL5 challenge. J. Comput. Aided Mol. Des. 30(11): 945-958 (2016) - [j6]Daniel L. Parton, Patrick B. Grinaway, Sonya M. Hanson, Kyle Beauchamp, John D. Chodera
Ensembler: Enabling High-Throughput Molecular Simulations at the Superfamily Scale. PLoS Comput. Biol. 12(6) (2016) - 2015
- [j5]Christian Kramer, John D. Chodera
Introduction to the special issue: Data Part 2: Experimental Data. J. Comput. Aided Mol. Des. 29(9): 777 (2015) - [j4]Sonya M. Hanson, Sean Ekins, John D. Chodera
Modeling error in experimental assays using the bootstrap principle: understanding discrepancies between assays using different dispensing technologies. J. Comput. Aided Mol. Des. 29(12): 1073-1086 (2015) - 2013
- [j3]Kai Wang, John D. Chodera
Identifying ligand binding sites and poses using GPU-accelerated Hamiltonian replica exchange molecular dynamics. J. Comput. Aided Mol. Des. 27(12): 989-1007 (2013)
2000 – 2009
- 2006
- [j2]John D. Chodera, William C. Swope, Jed W. Pitera, Ken A. Dill:
Long-Time Protein Folding Dynamics from Short-Time Molecular Dynamics Simulations. Multiscale Model. Simul. 5(4): 1214-1226 (2006) - 2003
- [j1]Chaok Seok, J. Ben Rosen, John D. Chodera
MOPED: Method for optimizing physical energy parameters using decoys. J. Comput. Chem. 24(1): 89-97 (2003)
Coauthor Index
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