Tri-substituted triazoles as potent non-nucleoside inhibitors of the HIV-1 reverse transcriptase.

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.

Hydrophobic acetal and ketal derivatives of mannopeptimycin-alpha and desmethylhexahydromannopeptimycin-alpha: semisynthetic glycopeptides with potent activity against Gram-positive bacteria.

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.

Thiourea inhibitors of herpes viruses. Part 2: N-Benzyl-N'-arylthiourea inhibitors of CMV.

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.

Thiourea inhibitors of herpes viruses. Part 1: bis-(aryl)thiourea inhibitors of CMV.

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.

Structure-based versus property-based approaches in the design of G-protein-coupled receptor-targeted libraries.

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. As a reference base, 3365 GPCR-active agents belonging to nine different GPCR classes were used. General rules were developed which enabled us to assess possible areas where both approaches would be useful. The predictability of the neural network algorithm based on 14 physicochemical descriptors was found to exceed the predictability of the similarity-based approach. The structural diversity of high-scored subsets obtained with the neural network-based method exceeded the diversity obtained with the similarity-based approach. In addition, the descriptor distributions of the compounds selected by the neural network algorithm more closely approximate the corresponding distributions of the real, active compounds than did those selected using the alternative method.

Pyrimido[1,2-b]-1,2,4,5-tetrazin-6-ones as HCMV protease inhibitors: a new class of heterocycles with flavin-like redox properties.

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.

Advanced exact structure searching in large databases of chemical compounds.

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. Though in the large registry databases, such as Beilstein and CAS, the tautomers are found in an automated fashion using high-performance computational technologies, their real-time recognition in the nonregistry corporate databases, as a rule, remains problematic. We have developed an effective algorithm for tautomer searching based on the proprietary chemoinformatics platform. This algorithm reduces the compound to a canonical structure. This feature enables rapid, automated computer searching of most of the known tautomeric transformations that occur in databases of organic compounds. Another useful extension of this methodology is related to the ability to effectively search for different forms of compounds that contain ionic and semipolar bonds. The computations are performed in the Windows environment on a standard personal computer, a very useful feature. The practical application of the proposed methodology is illustrated by several examples of successful recovery of tautomers and different forms of ionic compounds from real commercially available nonregistry databases.

Property-based design of GPCR-targeted library.

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. The method employed a set of descriptors for encoding the molecular structures and by training of a neural network for classifying the molecules. The molecular requirements were profiled and validated by using available databases of GPCR- and non-GPCR-active agents [5736 diverse GPCR-active molecules and 7506 diverse non-GPCR-active molecules from the Ensemble Database (Prous Science, 2002)]. The method enables efficient qualification or disqualification of a molecule as a potential GPCR ligand and represents a useful tool for constraining the size of GPCR-targeted libraries that will help speed up the development of new GPCR-active drugs.

Classification scheme for the design of serine protease targeted compound libraries.

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. The method enables an efficient qualification or disqualification of a molecule as a potential serine protease ligand. It represents a useful tool for constraining the size of virtual libraries that will help accelerate the development of new serine protease active drugs.