Showing papers by "Huafeng Xu published in 2002"

Can Water Polarizability Be Ignored in Hydrogen Bond Kinetics

Abstract: The kinetics of forming and breaking water−water hydrogen bonds in neat water, an aqueous solution of ethane, and an aqueous solution of NaCl are studied by molecular dynamics simulations. We compare nonpolarizable and polarizable water models to elucidate the effect of water's polarizability on hydrogen bonds. We find that polarizability strengthens the hydrogen bonds and increases the hydrogen bond relaxation time by a factor of between 50% and 100%. The Gibbs energy of activation for breaking hydrogen bonds is ∼0.2 kcal·mol-1 higher for the polarizable water model. Polarizability also causes the rate of forming and breaking hydrogen bonds to be more dependent on the local environment.

A self-organizing principle for learning nonlinear manifolds

Abstract: Modern science confronts us with massive amounts of data: expression profiles of thousands of human genes, multimedia documents, subjective judgments on consumer products or political candidates, trade indices, global climate patterns, etc. These data are often highly structured, but that structure is hidden in a complex set of relationships or high-dimensional abstractions. Here we present a self-organizing algorithm for embedding a set of related observations into a low-dimensional space that preserves the intrinsic dimensionality and metric structure of the data. The embedding is carried out by using an iterative pairwise refinement strategy that attempts to preserve local geometry while maintaining a minimum separation between distant objects. In effect, the method views the proximities between remote objects as lower bounds of their true geodesic distances and uses them as a means to impose global structure. Unlike previous approaches, our 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. More importantly, the method is found to scale linearly with the number of points and can be applied to very large data sets that are intractable by conventional embedding procedures.

Retrospect and prospect of virtual screening in drug discovery.

Abstract: We review the prominent technologies in virtual screening, and their applications in drug discovery.