Xiaohua Zhang

Contact information

Lawrence Livermore National Laboratory
7000 East Avenue, L-367
Livermore, CA 94550
email: zhang30@llnl.gov
phone: 925 422 2414


Ph.D.: Computational Chemistry - University of California, Santa Barbara - 2007


I develop novel applications of theoretical/computational chemistry methods and engineer software to implement these methods. My research interests include: (i) drug discovery and high-throughput drug screening C++ toolkit development; (ii) fragment- and structure-based drug design; (iii) high performance computing applied to computational chemistry; (iv) algorithm derivation and program engineering for molecular simulation.


ConveyorLC a pipeline to perform virtual screening

VinaLC a parallel molecular docking program based on AutoDock Vina

ddcMD a fully GPU-accelerated molecular dynamics program for the Martini force field

ddcMDconverter a python package to convert GROMACS files to ddcMD inputs


  1. D. H. Ahn, X. Zhang, J. Mast, S. Herbein, F. Di Natale, D. Kirshner, S. A. Jacobs, I. Karlin, D. J. Milroy, B. R. De Supinski, B. Van Essen, J. Allen, F. C. Lightstone (2022) Scalable Composition and Analysis Techniques for Massive Scientific WorkflowsIEEE 18th International Conference on e-Science (e-Science). 32-43, doi: 10.1109/eScience55777.2022.00018 (Won Best Paper).
  2. K. Nguyen, C. A. López, C. Neale, Q. N. Van, T. S. Carpenter, F. Di Natale, T. Travers, T. H. Tran, A. H. Chan, H. Bhatia, P. H. Frank, M. Tonelli, X. Zhang, G. Gulten, T.Reddy, V. Burns, T. Oppelstrup, N. Hengartner, D. K. Simanshu, P.-T. Bremer, D. Chen, J. N. Glosli, R. Shrestha, T. Turbyville, F. H. Streitz, D. V. Nissley, H. I. Ingólfsson, A. G. Stephen, F. C. Lightstone, S. Gnanakaran (2022) Exploring CRD mobility during RAS/RAF engagement at the membrane. Biophys. J., 121, 19, 3630.
  3. C. A. Lopez, X. Zhang, F. Aydin, R. Shrestha, Q. N. Van, C. B. Stanley, T. S. Carpenter, K. Nguyen, L. A. Patel, D. Chen, V. Burns, N. W. Hengartner, T. J. E. Reddy, H. Bhatia, F. Di Natale, T. H. Tran, A. H. Chan, D. K. Simanshu, D. V. Nissley, F. H. Streitz, A. G. Stephen, T. J. Turbyville, F. C. Lightstone, S. Gnanakaran, H. I Ingolfsson, C. Neale (2022) Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework. J. Chem. Theory Comput., 18 (8), 5025-5045.
  4. H. I. Ingólfsson, C. Neale, T. S. Carpenter, R. Shrestha, C. A. López, T. H. Tran, T. Oppelstrup, H. Bhatia, L. G. Stanton, X.Zhang, S. Sundram, F. Di Natale, A. Agarwal, G. Dharuman, S. I. L. Kokkila Schumacher, . Turbyville, G. Gulten, Q. N. Van, D. Goswami, F. Jean-Francois, C. Agamasu, D. Chen, J. J. Hettige, T. Travers, S. Sarkar, M. P. Surh, Y. Yang, A. Moody, S. Liu, B. C. Van Essen, A. F. Voter, A. Ramanathan, N.W. Hengartner, D. K. Simanshu, A. G. Stephen,  P.-T. Bremer, S. Gnanakaran, J. N. Glosli, F. C. Lightstone, F. McCormick,  D. V. Nissley, and F. H. Streitz (2022) Machine learning–driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteinsProceedings of the National Academy of Sciences, Jan 2022, 119 (1) e2113297119.
  5. H. Bhatia, F. Di Natale, J. Y. Moon, X. Zhang, J. R. Chavez, F. Aydin, C. Stanley, T. Oppelstrup, C. Neale, S. Kokkila Schumacher, D. H. Ahn, S. Herbein, T. S. Carpenter, S. Gnanakaran, P.-T. Bremer, J. N. Glosli, F. C. Lightstone, H. I. Ingólfsson (2021) Generalizable coordination of large multiscale workflows: challenges and learnings at scale, SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, Article No.: 10, 1-16.
  6. G. A. Stevenson, D. Jones, H. Kim, W. F. D. Bennett, B. J. Bennion, M. Borucki, F. Bourguet, A. Epstein, M. Franco, B. Harmon, S. He, M. P. Katz, D. Kirshner, V. Lao, E. Y. Lau, J. Lo, K. McLoughlin, R. Mosesso, D. K. Murugesh, O. A. Negrete, E. A. Saada, B. Segelke, M. Stefan, M. W. Torres, D. Weilhammer, S. Wong, Y. Yang, A. Zemla, X. Zhang, F. Zhu, F. C. Lightstone, J. E. Allen (2021) High-throughput virtual screening of small molecule inhibitors for SARS-CoV-2 protein targets with deep fusion models, SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, Article No.: 74, 1–13.
  7. E. Y. Lau, O. A. Negrete, W. F. D.Bennett, B. J. Bennion, M. Borucki, F. Bourguet, A. Epstein, M. Franco, B. Harmon, S. He, D. Jones, H. Kim, D. Kirshner, V. Lao, J. Lo, K. McLoughlin, R. Mosesso, D. K. Murugesh, E. A. Saada, B. Segelke, M. A. Stefan, G. A. Stevenson, M. W. Torres, D. R. Weilhammer, S. Wong, Y. Yang, A. Zemla, X. Zhang, F. Zhu, J. E. Allen, F. C. Lightstone (2021) Discovery of Small-molecule Inhibitors of SARS-CoV-2 Proteins Using a Computational and Experimental Pipeline, Frontiers in Molecular Biosciences, 8, 644.
  8. D. Jones, H. Kim, X. Zhang, A. Zemla, G. Stevenson, W. F. D. Bennett, D. Kirshner, S. E. Wong, F. C. Lightstone, and J. E. Allen (2021) Improved Protein–Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference, J. Chem. Info. Model., 61, 4, 1583–1592.
  9. X. Zhang, S. Sundram, T. Oppelstrup, S. I. L. Kokkila-Schumacher, T. S. Carpenter, H. I. Ingolfsson, F. H. Streitz, F. C. Lightstone, J. N. Glosli (2020)  ddcMD:A Fully GPU-accelerated Molecular Dynamics Program for the Martini Force Field. J. Chem. Phys., 153, 045103.
  10. F. Zhu, X. Zhang, J. E. Allen, D. Jones, F. C. Lightstone (2020) Binding Affinity Prediction by Pairwise Function Based on Neural Network. J. Chem. Inf. Model. 60, 2766-2772.
  11. F. Di Natale, H. Bhatia, T. S. Carpenter, C. Neale, S. Kokkila Schumacher, T. Oppelstrup, L. Stanton, X. Zhang, S. Sundram, T. R. W. Scogland, G. Dharuman, M. P. Surh, Y. Yang, C. Misale, L. Schneidenbach, C. Costa, C. Kim, B. D’Amora, S. Gnanakaran, D. V. Nissley, F. Streitz, F. C. Lightstone, P.-T. Bremer, J. N. Glosli, H. I. Ingolfsson (2019) A Massively Parallel Infrastructure for Adaptive Multiscale Simulations: Modeling RAS Initiation Pathway for Cancer. SC '19: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, Article No.: 57, 1–16. (Won Best Paper) 
  12. Ian Karlin, et al. (2019) Preparation and Optimization of a Diverse Workload for a Large-Scale Heterogeneous System. SC '19: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. https://doi.org/10.1145/3295500.3356192
  13. Jonathan P. Cranford, Thomas J. O’Hara, Christopher T. Villongco, Omar M. Hafez, Robert C. Blake, Joseph Loscalzo, Jean–Luc Fattebert, David F. Richards, Xiaohua Zhang, James N. Glosli, Andrew D. McCulloch, David E. Krummen, Felice C. Lightstone, and Sergio E. Wong. (2018) Efficient computational modeling of human ventricular activation and its electrocardiographic representation: A sensitivity study. Cardiovascular Engineering and Technology, 9, 447–467.
  14. X. Zhang, H. Péréz-Sánchez, and F. C. Lightstone. (2017) A Comprehensive Docking and MM/GBSA Rescoring Study of Ligand Recognition upon Binding Antithrombin. Current Topics in Medicinal Chemistry, 17, 1-9.
  15. X. Zhang, H. Péréz-Sánchez, and F. C. Lightstone. (2015) Molecular Dynamics Simulations of Ligand Recognition Upon Binding Antithrombin: A MM/GBSA Approach. Bioinformatics and Biomedical Engineering, 9044, 584-593.
  16. M. X. LaBute, X. Zhang, J. Lenderman, B. J. Bennion, S. E. Wong, F. C. Lightstone (2014) Adverse Drug Reaction Prediction Using Scores Produced by Large-Scale Drug-Protein Target Docking on High-Performance Computing Machines. PLoS ONE, 9(9): e106298.
  17. X. Zhang, S. E. Wong, F. C. Lightstone (2014) Toward Fully Automated High Performance Computing Drug Discovery: A Massively Parallel Virtual Screening Pipeline for Docking and Molecular Mechanics/Generalized Born Surface Area Rescoring to Improve Enrichment. J. Chem. Inf. Model., 54(1) 324-337.
  18. X. Zhang, S. E. Wong, F. C. Lightstone (2013) Message Passing Interface and Multithreading Hybrid for Parallel Molecular Docking of Large Databases on Petascale High Performance Computing Machines. J. Comput. Chem., 34, 915-927.
  19. R. Custelcean, P. V. Bonnesen, N. C. Duncan, X. Zhang, L. A. Watson, G. Van Berkel, W. B. Parson, and B. P. Hay. (2012) Urea-Functionalized M4L6 Cage Receptors: Anion-Templated Self-Assembly and Selective Guest Exchange in Aqueous Solutions, J. Am. Chem. Soc. 134, 8525-8534.
  20. J. Nadas, X. Zhang, and B. P. Hay. (2011) Shapes of Sulfur, Oxygen, and Nitrogen Mustards, J. Phys. Chem. A 115, 6709-6716.
  21. X. Zhang, A. C. Gibbs, C. H. Reynolds, M. B. Peters, and L. M. Westerhoff. (2010) Quantum Mechanical Pairwise Decomposition Analysis of Protein Kinase B Inhibitors: Validating a New Tool for Guiding Drug Design, J. Chem. Inf. Model. 50, 651-661.
  22. D. J. Diller, C. Humblet, X. Zhang, and L. M. Westerhoff. (2010) Computational alanine scanning with linear scaling semiempirical quantum mechanical methods, Proteins-Struct. Funct. Bioinf. 78, 2329-2337.
  23. X. Zhang, and T. C. Bruice. (2008) Complexation of single strand telomere and telomerase RNA template polyanions by deoxyribonucleic guanidine (DNG) polycations: Plausible anticancer agents, Biorg. Med. Chem. Lett. 18, 665-669.
  24. X. Zhang, and T. C. Bruice. (2007) Diels-Alder ribozyme catalysis: A computational approach, J. Am. Chem. Soc. 129, 1001-1007.
  25. X. Zhang, and T. C. Bruice. (2007) Temperature-dependent structure of the ES complex of Bacillus stearothermophilus alcohol dehydrogenase, Biochemistry. 46, 837-843.
  26. X. Zhang, and T. C. Bruice. (2006) Temperature dependence of the structure of the substrate and active site of the Thermus thermophilus chorismate mutase ES complex, Biochemistry. 45, 8562-8567.
  27. X. Zhang, and T. C. Bruice. (2005) The proficiency of a thermophilic chorismate mutase enzyme is solely through an entropic advantage in the enzyme reaction, Proc. Natl. Acad. Sci. U. S. A. 102, 18356-18360
  28. X. D. Zhang, X. Zhang, and T. C. Bruice. (2005) A definitive mechanism for chorismate mutase, Biochemistry. 44, 10443-10448.
  29. D. F. Liu, A. B. Seuthe, O. T. Ehrler, X. Zhang, T. Wyttenbach, J. F. Hsu, and M. T. Bowers. (2005) Oxytocin-receptor binding: Why divalent metals are essential, J. Am. Chem. Soc. 127, 2024-2025.