J
Junmei Wang
Researcher at University of Pittsburgh
Publications - 189
Citations - 37934
Junmei Wang is an academic researcher from University of Pittsburgh. The author has contributed to research in topics: Chemistry & Medicine. The author has an hindex of 52, co-authored 162 publications receiving 31230 citations. Previous affiliations of Junmei Wang include University of California, San Francisco & University of California, San Diego.
Papers
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Journal ArticleDOI
Applications of genetic algorithms on the structure–activity correlation study of a group of non-nucleoside HIV-1 inhibitors
Tingjun Hou,Junmei Wang,X. J. Xu +2 more
TL;DR: A QSAR program combining genetic algorithm with multiple linear regression and cross-validation is developed and it is found that low lipophilicity at C-6, small compounds surface, high π electron density of the benzo fused ring and low dipole along the z axis were the most important factors that may influence the RT activities.
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Development and Evaluation of MM/GBSA Based on a Variable Dielectric GB Model for Predicting Protein-Ligand Binding Affinities.
TL;DR: It is found that rational truncation of protein residues far from the binding site can significantly speed up theMM/GBSAVDGB calculations while almost does not influence the prediction accuracy, and it is feasible to implement the system-truncated MM/GB SAVDGB as a scoring function for SBDD.
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Development of Polarizable Gaussian Model for Molecular Mechanical Calculations I: Atomic Polarizability Parameterization To Reproduce ab Initio Anisotropy.
TL;DR: The new pGM and Thole models notably improve molecular polarization anisotropy, taking advantage of its ability to attenuate all short-range electrostatic interactions by fitting the ab initio molecular polarizability tensors calculated at the B3LYP/aug-cc-pVTZ level.
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A rule-based algorithm for automatic bond type perception
TL;DR: An automatic algorithm for assigning chemical connectivity and bond order regardless of hydrogen for organic molecules is provided, and only three dimensional coordinates and element identities are needed for the algorithm.
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A Continuum Poisson–Boltzmann Model for Membrane Channel Proteins
TL;DR: The new continuum membrane model is optimized to best reproduce the distributions of buried water molecules in the membrane region as sampled in explicit water simulations and shows that the widely adopted water probe of 1.4 Å for globular proteins is a very reasonable default value for membrane protein simulations.