Publications | Ding Group at the Tufts University

1: Optimizing Force Fields with Experimental Data Using Ensemble Reweighting and Potential Contrasting
Xinqiang Ding
preprint. 2024,

2: Bayesian Multistate Bennett Acceptance Ratio Methods
Xinqiang Ding
J. Chem. Theory Comput. 2024, 20, 1878–1888

Before establishing the Ding Group

3: Fast Free Energy Estimates from λ-dynamics with Bias-updated Gibbs Sampling
Michael Robo, Ryan Hayes, Xinqiang Ding, Brian Pulawski, Jonah Vilseck
Nat. Commun. 2023, 14, 8515

4: Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks
Justin Airas, Xinqiang Ding, Bin Zhang
ACS Cent. Sci.. 2023,

5: OpenABC enables flexible, simplified, and efficient GPU accelerated simulations of biomolecular condensates
Shuming Liu, Cong Wang, Andrew P Latham, Xinqiang Ding, Bin Zhang
PLoS Comput. Biol.. 2023, 19, e1011442

6: Decorrelating Hi-C Contacts Enhances Identification of the Interactions Driving Genomic Structure and Organization
Greg Schuette, Xinqiang Ding, Bin Zhang
Biophys. J.. 2023, 122, 494a

7: Contrasting Learning of Coarse-Grained Force Fields
Xinqiang Ding, Bin Zhang
J. Chem. Theory Comput. 2022, 18, 6334-6344

8: Generalizing the Discrete Gibbs Sampler-Based λ-Dynamics Approach for Multisite Sampling of Many Ligands
Jonah Z. Vilseck, Xinqiang Ding, Ryan L. Hayes, Charles L. Brooks III
J. Chem. Theory Comput. 2021, 17, 3895-3907

9: DeepBAR: A Fast and Exact Method for Binding Free Energy Computation
Xinqiang Ding, Bin Zhang
J. Phys. Chem. Lett. 2021, 12, 2509-2515

10: Stability and folding pathways of tetra-nucleosome from six-dimensional free energy surface
Xinqiang Ding, Xingcheng Lin, Bin Zhang
Nat. Commun. 2021, 12, 1091

11: Learning Deep Generative Models with Annealed Importance Sampling
Xinqiang Ding, David J. Freedman
Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020). 2020,

12: Computing Absolute Free Energy with Deep Generative Models
Xinqiang Ding, Bin Zhang
J. Phys. Chem. B. 2020, 124, 10166-10172

13: Accelerated CDOCKER with GPUs, parallel simulated annealing and fast Fourier transforms
Xinqiang Ding, Yujin Wu, Yanming Wang, Jonah Z. Vilseck, Charles L. Brooks III
J. Chem. Theory Comput. 2020, 16, 3910-3919

14: Deciphering Protein Evolution and Fitness Landscapes with Latent Space Models
Xinqiang Ding, Zhengting Zou, Charles L. Brooks III
Nat. Commun. 2019, 10, 5644

15: Fast Solver for Large Scale Multistate Bennett Acceptance Ratio Equations
Xinqiang Ding, Jonah Z. Vilseck, Charles L. Brooks III
J. Chem. Theory Comput. 2019, 15, 799-802

16: CDOCKER and λ-dynamics for Prospective Prediction in D3R Grand Challenge 2
Xinqiang Ding, Ryan L. Hayes, Jonah Z. Vilseck, Murchtricia K. Charles, Charles L. Brooks
J. Comput. Aided Mol. Des.. 2018, 32, 89-102

17: Gibbs Sampler-based λ-dynamics and Rao–Blackwell Estimator for Alchemical Free Energy Calculation
Xinqiang Ding, Jonah Z. Vilseck, Ryan L. Hayes, Charles L. Brooks III
J. Chem. Theory Comput. 2017, 13, 2501-2510

18: Mechanism of Vps4 Hexamer Function Revealed by Cryo-EM
Min Su, Emily Z Guo, Xinqiang Ding, Yan Li, Jeffrey T Tarrasch, Charles L Brooks, Zhaohui Xu", Georgios Skiniotis
Sci. Adv. 2017, 3, e1700325

19: Improved Prediction of RNA Secondary Structure by Integrating the Free Energy Model with Restraints Derived from Experimental Probing Data
Yang Wu, Binbin Shi, Xinqiang Ding, Tong Liu, Xihao Hu, Kevin Y. Yip, Zheng Rong Yang, David H. Mathews, Zhi John Lu
Nucleic Acids Res. 2015, 15, 7247-7259