Publications by authors named "Stanley A Lang"

A new series of 1,2,4-triazoles was synthesized and tested against several NNRTI-resistant HIV-1 isolates. Several of these compounds exhibited potent antiviral activities against efavirenz- and nevirapine-resistant viruses, containing K103N and/or Y181C mutations or Y188L mutation. Triazoles were first synthesized from commercially available substituted phenylthiosemicarbazides, then from isothiocyanates, and later by condensing the desired substituted anilines with thiosemicarbazones.

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The effect of introducing hydrophobic groups onto the disaccharide portion of the mannopeptimycins has been examined. Under acid-catalyzed conditions dimethyl acetals and ketals react on the terminal mannose of the disaccharide moiety of mannopeptimycin-alpha and the cyclohexylalanyl analogue 2. The preferentially formed monofunctionalized 4,6-acetals and -ketals display potent antibacterial activities against Gram-positive microorganisms, including MRSA, PRSP, and VRE pathogens.

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A series of highly potent thiourea inhibitors of cytomegalovirus (CMV) with improved stability properties was prepared and evaluated. Compound 29 inhibited the virus in cultured HFF cells with IC50 of 0.2 nM.

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Bis-(aryl)thioureas were found to be potent and selective inhibitors of cytomegalovirus (CMV) in cultured HFF cells. Of these, the thiazole analogue 38 was investigated as a potential development candidate.

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The synthesis and biological activity of pyrimidotetrazin-6-ones against HCMV protease is described. The mechanism of action for these inhibitors is the oxidation of several cysteine residues to generate cross-linked enzyme.

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In this work, two alternative approaches to the design of small-molecule libraries targeted for several G-protein-coupled receptor (GPCR) classes were explored. The first approach relies on the selection of structural analogues of known active compounds using a substructural similarity method. The second approach, based on an artificial neural network classification procedure, searches for compounds that possess physicochemical properties typical of the GPCR-specific agents.

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The development of a scoring scheme for the classification of molecules into serine protease (SP) actives and inactives is described. The method employed a set of pre-selected descriptors for encoding the molecular structures, and a trained neural network for classifying the molecules. The molecular requirements were profiled and validated by using available databases of SP- and non-SP-active agents [1,439 diverse SP-active molecules, and 5,131 diverse non-SP-active molecules from the Ensemble Database (Prous Science, 2002)] and Sensitivity Analysis.

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Efficient recognition of tautomeric compound forms in large corporate or commercially available compound databases is a difficult and labor intensive task. Our data indicate that up to 0.5% of commercially available compound collections for bioscreening contain tautomers.

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The design of a GPCR-targeted library, based on a scoring scheme for the classification of molecules into "GPCR-ligand-like" and "non-GPCR-ligand-like", is outlined. The methodology is a valuable tool that can aid in the selection and prioritization of potential GPCR ligands for bioscreening from large collections of compounds. It is based on the distillation of knowledge from large databases of GPCR and non-GPCR active agents.

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