H
Huafeng Xu
Researcher at D. E. Shaw Research
Publications - 52
Citations - 6862
Huafeng Xu is an academic researcher from D. E. Shaw Research. The author has contributed to research in topics: Molecular dynamics & Chemistry. The author has an hindex of 27, co-authored 45 publications receiving 5545 citations. Previous affiliations of Huafeng Xu include University of California, San Francisco & Columbia University.
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Journal ArticleDOI
Mechanism of Na+/H+ antiporting.
Isaiah T. Arkin,Huafeng Xu,Morten Ø. Jensen,Eyal Arbely,Estelle R. Bennett,Kevin J. Bowers,Edmond Chow,Ron O. Dror,Michael P. Eastwood,Ravenna Flitman-Tene,Brent A. Gregersen,John L. Klepeis,István Kolossváry,Yibing Shan,David E. Shaw,David E. Shaw +15 more
TL;DR: Two protons are required to transport a single Na+ ion: D163 protonates to reveal the Na+-binding site to the periplasm, and subsequent protonation of D164 releases Na+.
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Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery.
Tai-Sung Lee,Bryce K. Allen,Timothy J. Giese,Zhenyu Guo,Pengfei Li,Charles Y. Lin,T. Dwight McGee,David A. Pearlman,Brian K. Radak,Yujun Tao,Hsu Chun Tsai,Huafeng Xu,Woody Sherman,Darrin M. York +13 more
TL;DR: A contemporary overview of the scientific, technical, and practical issues associated with running relative BFE simulations in AMBER20 is provided, with a focus on real-world drug discovery applications.
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Efficient multiple time step method for use with Ewald and particle mesh Ewald for large biomolecular systems
TL;DR: In this paper, the particle-particle particle-mesh (P3M) method is combined with the reversible reference system propagator algorithm (RESPA) for treating the multiple time scale problems in the molecular dynamics of complex systems with multiple time scales and long-range forces.
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Quantitative Characterization of the Binding and Unbinding of Millimolar Drug Fragments with Molecular Dynamics Simulations.
TL;DR: Long time scale fragment binding simulations of sufficient length are performed, allowing the binding affinities, on- and off-rates, and relative occupancies of alternative binding sites and alternative poses within each binding site to be estimated, thereby illustrating the potential of long time scale MD as a quantitative tool for fragment-based drug discovery.
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A self-organizing principle for learning nonlinear manifolds
TL;DR: The method can reveal the underlying geometry of the manifold without intensive nearest-neighbor or shortest-path computations and can reproduce the true geodesic distances of the data points in the low-dimensional embedding without requiring that these distances be estimated from the data sample.