Author

L.-P. Wang

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biophysics· 4

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Approach:deep learning· 2 Research Focus:benchmarking· 1 Approach:experimental assay· 1 Research Focus:protein folding & dynamics· 1 Assay:protein sequence· 1 Research Focus:software / tool· 1

Frequent Co-Authors

C. D. Stern· 2 shared J. D. Chodera· 2 shared A. C. Simmonett· 1 shared A. Liwang· 1 shared B. R. Brooks· 1 shared C. I. Bayly· 1 shared D. G. A. Smith· 1 shared D. L. Mobley· 1 shared

Activity by Year

2017-2020 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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2 shared papersBiophysics

C. D. Stern

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Author profile →

4 papers

Recent·Most cited

Capturing non-local through-bond effects in molecular mechanics force fields I: Fragmenting molecules for quantum chemical torsion scans

C. D. Stern, C. I. Bayly, D. G. A. Smith, J. Fass, L.-P. Wang, D. L. Mobley, J. D. Chodera

The paper introduces a method to assess and improve fragmentation schemes for quantum chemical torsion scans by using fractional Wiberg bond orders to preserve chemical environment accuracy.

bioRxiv · Aug 27, 2020 · PreprintBiophysics

The Automated Optimisation of a Coarse-Grained Force Field Using Free Energy Data

J. Caceres-Delpiano, L.-P. Wang, J. W. Essex

This paper introduces an automated method to optimise coarse-grained force fields using free energy data from atomistic simulations, improving their accuracy for protein systems.

bioRxiv · Aug 13, 2020Biophysics

Sequence-based Prediction of Metamorphic Behavior in Proteins

N. Chen, M. Das, A. Liwang, L.-P. Wang

This paper introduces a sequence-based computational model to predict metamorphic behavior in proteins using diversity indices derived from secondary structure predictions, achieving a Matthews correlation coefficient of ~0.4.

bioRxiv · Feb 27, 2020Biophysics

OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics

P. Eastman, J. Swails, J. D. Chodera, R. T. McGibbon, Y. Zhao, K. A. Beauchamp, L.-P. Wang, A. C. Simmonett, M. P. Harrigan, C. D. Stern, R. P. Wiewiora, B. R. Brooks, V. S. Pande

OpenMM 7 enables rapid development of high-performance molecular dynamics algorithms with extensible features that work across hardware types without modifying the core toolkit.

bioRxiv · Jun 28, 2017Biophysics