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Vincent Voelz

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Vincent Voelz

  • College of Science and Technology

    • Chemistry

      • Professor

Courses Taught

Number

Name

Level

CHEM 4401

Biochemistry I

Undergraduate

CHEM 5412

Structural Bioinformatics II

Graduate

Selected Publications

Recent

  • Raddi, R.M., Marshall, T., & Voelz, V.A. (2026). Automatic forward model parameterization with Bayesian inference of conformational populations. APL Mach Learn, 4(1), 016102. United States. 10.1063/5.0287423

  • Novack, D., Zhang, S., & Voelz, V.A. (2025). Massively Parallel Free Energy Calculations for In Silico Affinity Maturation of Designed Miniproteins. J Chem Theory Comput, 21(16), 8034-8050. United States. 10.1021/acs.jctc.5c00703

  • Cavender, C.E., Case, D.A., Chen, J.C., Chong, L.T., Keedy, D.A., Lindorff-Larsen, K., Mobley, D.L., Ollila, O.S., Oostenbrink, C., Robustelli, P., Voelz, V.A., Wall, M.E., Wych, D.C., & Gilson, M.K. (2025). Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields [Article v1.0]. Living J Comput Mol Sci, 6(1). United States. 10.33011/livecoms.6.1.3871

  • Raddi, R.M., Marshall, T., Ge, Y., & Voelz, V.A. (2025). Model Selection Using Replica Averaging with Bayesian Inference of Conformational Populations. J Chem Theory Comput, 21(12), 5880-5889. United States. 10.1021/acs.jctc.5c00044

  • Nguyen, T.D., Raddi, R.M., & Voelz, V.A. (2025). High-Resolution Tuning of Non-Natural and Cyclic Peptide Folding Landscapes against NMR Measurements Using Markov Models and Bayesian Inference of Conformational Populations. J Chem Theory Comput, 21(12), 6213-6225. United States. 10.1021/acs.jctc.5c00489

  • Novack, D., Raddi, R.M., Zhang, S., Hurley, M.F., & Voelz, V.A. (2025). Simple Method to Optimize the Spacing and Number of Alchemical Intermediates in Expanded Ensemble Free Energy Calculations. J Chem Inf Model, 65(12), 6089-6101. United States. 10.1021/acs.jcim.5c00704

  • Cavender, C.E., Case, D.A., Chen, J.C., Chong, L.T., Keedy, D.A., Lindorff-Larsen, K., Mobley, D.L., Ollila, O.S., Oostenbrink, C., Robustelli, P., Voelz, V.A., Wall, M.E., Wych, D.C., & Gilson, M.K. (2025). Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields [Article v0.1]. ArXiv. United States. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/40196146.

  • Goold, S.R., Raddi, R.M., & Voelz, V.A. (2025). Expanded ensemble predictions of toluene-water partition coefficients in the SAMPL9 log P challenge. Phys Chem Chem Phys, 27(12), 6005-6013. England. 10.1039/d4cp03621b

  • Zhang, S., Ge, Y., & Voelz, V.A. (2024). Improved Estimates of Folding Stabilities and Kinetics with Multiensemble Markov Models. Biochemistry, 63(22), 3045-3056. United States. 10.1021/acs.biochem.4c00573

  • Novack, D., Zhang, S., & Voelz, V.A. (2024). Massively parallel free energy calculations for in silico affinity maturation of designed miniproteins. BioRxiv. United States. 10.1101/2024.05.17.594758

  • Raddi, R.M. & Voelz, V.A. (2023). Markov State Model of Solvent Features Reveals Water Dynamics in Protein-Peptide Binding. J Phys Chem B, 127(50), 10682-10690. United States. 10.1021/acs.jpcb.3c04775

  • Hurley, M.F., Raddi, R.M., Pattis, J.G., & Voelz, V.A. (2023). Expanded ensemble predictions of absolute binding free energies in the SAMPL9 host-guest challenge. Phys Chem Chem Phys, 25(47), 32393-32406. England. 10.1039/d3cp02197a

  • Raddi, R.M., Ge, Y., & Voelz, V.A. (2023). BICePs v2.0: Software for Ensemble Reweighting Using Bayesian Inference of Conformational Populations. J Chem Inf Model, 63(8), 2370-2381. United States. 10.1021/acs.jcim.2c01296

  • Laye, V.J., Solieva, S., Voelz, V.A., & DasSarma, S. (2022). Effects of Salinity and Temperature on the Flexibility and Function of a Polyextremophilic Enzyme. Int J Mol Sci, 23(24). Switzerland. 10.3390/ijms232415620

  • Novack, D., Qian, L., Acker, G., Voelz, V.A., & Baxter, R.H. (2022). Oncogenic Mutations in the DNA-Binding Domain of FOXO1 that Disrupt Folding: Quantitative Insights from Experiments and Molecular Simulations. Biochemistry, 61(16), 1669-1682. United States. 10.1021/acs.biochem.2c00224

  • Ge, Y. & Voelz, V.A. (2022). Estimation of binding rates and affinities from multiensemble Markov models and ligand decoupling. J Chem Phys, 156(13), 134115. United States. 10.1063/5.0088024

  • Novack, D., Qian, L., Acker, G., Voelz, V.A., & Baxter, R.H. (2022). Oncogenic mutations in the DNA-binding domain of FOXO1 disrupt folding: quantitative insights from experiments and molecular simulations. doi: 10.1101/2022.04.01.486713.

  • Zhang, S., Hahn, D.F., Shirts, M.R., & Voelz, V.A. (2021). Expanded Ensemble Methods Can be Used to Accurately Predict Protein-Ligand Relative Binding Free Energies. J Chem Theory Comput, 17(10), 6536-6547. United States. 10.1021/acs.jctc.1c00513

  • Raddi, R.M. & Voelz, V.A. (2021). Stacking Gaussian processes to improve p K a predictions in the SAMPL7 challenge. J Comput Aided Mol Des, 35(9), 953-961. Netherlands. 10.1007/s10822-021-00411-8

  • Nigam, A., Pollice, R., Hurley, M.F., Hickman, R.J., Aldeghi, M., Yoshikawa, N., Chithrananda, S., Voelz, V.A., & Aspuru-Guzik, A. (2021). Assigning confidence to molecular property prediction. Expert Opin Drug Discov, 16(9), 1009-1023. England. 10.1080/17460441.2021.1925247

  • Zimmerman, M.I., Porter, J.R., Ward, M.D., Singh, S., Vithani, N., Meller, A., Mallimadugula, U.L., Kuhn, C.E., Borowsky, J.H., Wiewiora, R.P., Hurley, M.F., Harbison, A.M., Fogarty, C.A., Coffland, J.E., Fadda, E., Voelz, V.A., Chodera, J.D., & Bowman, G.R. (2021). SARS-CoV-2 simulations go exascale to predict dramatic spike opening and cryptic pockets across the proteome. Nat Chem, 13(7), 651-659. England. 10.1038/s41557-021-00707-0

  • Hurley, M.F., Northrup, J.D., Ge, Y., Schafmeister, C.E., & Voelz, V.A. (2021). Metal Cation-Binding Mechanisms of Q-Proline Peptoid Macrocycles in Solution. J Chem Inf Model, 61(6), 2818-2828. United States. 10.1021/acs.jcim.1c00447

  • Ge, Y. & Voelz, V. (2021). Estimation of Binding Rates and Affinities from Multiensemble Markov Models and Ligand Decoupling. doi: 10.26434/chemrxiv.14728206.v1.

  • Raddi, R. & Voelz, V. (2021). Stacking Gaussian Processes to Improve pKa Predictions in the SAMPL7 Challenge. doi: 10.26434/chemrxiv.14650302.v1.

  • Ge, Y., Zhang, S., Erdelyi, M., & Voelz, V.A. (2021). Solution-State Preorganization of Cyclic β-Hairpin Ligands Determines Binding Mechanism and Affinities for MDM2. J Chem Inf Model, 61(5), 2353-2367. United States. 10.1021/acs.jcim.1c00029

  • Northrup, J.D., Wiener, J.A., Hurley, M.F., Hou, C.D., Keller, T.M., Baxter, R.H., Zdilla, M.J., Voelz, V.A., & Schafmeister, C.E. (2021). Metal-Binding Q-Proline Macrocycles. J Org Chem, 86(6), 4867-4876. United States. 10.1021/acs.joc.1c00116

  • Hurley, M., Northrup, J., Ge, Y., Schafmeister, C., & Voelz, V. (2021). Metal Cation-Binding Mechanisms of Q-Proline Peptoid Macrocycles in Solution. doi: 10.26434/chemrxiv.13567853.v1.

  • Ge, Y. & Voelz, V.A. (2021). Markov State Models to Elucidate Ligand Binding Mechanism. In 10.1007/978-1-0716-1209-5_14

  • Voelz, V.A., Ge, Y., & Raddi, R.M. (2021). Reconciling Simulations and Experiments With BICePs: A Review. Front Mol Biosci, 8, 661520. Switzerland. 10.3389/fmolb.2021.661520