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Xiang Simon Wang
Researcher at Howard University
Publications - 34
Citations - 738
Xiang Simon Wang is an academic researcher from Howard University. The author has contributed to research in topics: Virtual screening & Quantitative structure–activity relationship. The author has an hindex of 16, co-authored 34 publications receiving 643 citations. Previous affiliations of Xiang Simon Wang include University of Washington & University of North Carolina at Chapel Hill.
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Journal ArticleDOI
Novel Inhibitors of Human Histone Deacetylase (HDAC) Identified by QSAR Modeling of Known Inhibitors, Virtual Screening, and Experimental Validation
Hao Tang,Xiang Simon Wang,Xi Ping Huang,Bryan L. Roth,Kyle V. Butler,Alan P. Kozikowski,Mira Jung,Alexander Tropsha +7 more
TL;DR: This study illustrates the power of the combined QSAR-VS method as a general approach for the effective identification of structurally novel bioactive compounds.
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Identification of putative agouti-related protein(87-132)-melanocortin-4 receptor interactions by homology molecular modeling and validation using chimeric peptide ligands.
Andrzej Wilczynski,Xiang Simon Wang,Christine G. Joseph,Zhimin Xiang,Rayna M. Bauzo,Joseph W. Scott,Nicholas B. Sorensen,Amanda M. Shaw,William J. Millard,Nigel G. J. Richards,Carrie Haskell-Luevano +10 more
TL;DR: A three-dimensional homology molecular model of the mouse MC4 receptor complex with the hAGRP(87-132) ligand docked into the receptor has been developed to identify putative antagonist ligand-receptor interactions and the identification of a novel subnanomolar melanocortin peptide template Tyr-c[Asp-His-DPhe-Arg-Trp-Asn-Ala-Phe-Dpr]-Tyr-NH(2).
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Cheminformatics meets molecular mechanics: a combined application of knowledge-based pose scoring and physical force field-based hit scoring functions improves the accuracy of structure-based virtual screening.
Jui-Hua Hsieh,Shuangye Yin,Xiang Simon Wang,Shubin Liu,Nikolay V. Dokholyan,Alexander Tropsha +5 more
TL;DR: It is demonstrated that the use of target-specific pose (scoring) filter in combination with a physical force field-based scoring function (MedusaScore) leads to significant improvement of hit rates in VS studies for 12 of the 13 benchmark sets from the clustered version of the Database of Useful Decoys (DUD).
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An Unbiased Method To Build Benchmarking Sets for Ligand-Based Virtual Screening and its Application To GPCRs
TL;DR: The method has greatly reduced the “artificial enrichment” and “analogue bias” of a published GPCRs benchmarking set, i.e., GPCR Ligand Library (GLL)/GPCR Decoy Database (GDD), and addressed an important issue about the ratio of decoys per ligand.
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Differentiation of AmpC beta-lactamase binders vs. decoys using classification kNN QSAR modeling and application of the QSAR classifier to virtual screening
TL;DR: These studies suggest that validated QSAR models could complement structure based docking and scoring approaches in identifying promising hits by virtual screening of molecular libraries and question as to whether true binders and decoys could be distinguished based only on their structural chemical descriptors.