The identification of small-molecule modulators of protein function, and the process of transforming these into high-content lead series, are key activities in modern drug discovery. The decisions taken during this process have far-reaching consequences for success later in lead optimization and even more crucially in clinical development. Recently, there has been an increased focus on these activities due to escalating downstream costs resulting from high clinical failure rates. In addition, the vast emerging opportunities from efforts in functional genomics and proteomics demands a departure from the linear process of identification, evaluation and refinement activities towards a more integrated parallel process. This calls for flexible, fast and cost-effective strategies to meet the demands of producing high-content lead series with improved prospects for clinical success.

We report the discovery of a new monomeric peptide that reduces body weight and diabetic complications in rodent models of obesity by acting as an agonist at three key metabolically-related peptide hormone receptors: glucagon-like peptide-1 (GLP-1), glucose-dependent insulinotropic polypeptide (GIP) and glucagon receptors. This triple agonist demonstrates supraphysiological potency and equally aligned constituent activities at each receptor, all without cross-reactivity at other related receptors. Such balanced unimolecular triple agonism proved superior to any existing dual coagonists and best-in-class monoagonists to reduce body weight, enhance glycemic control and reverse hepatic steatosis in relevant rodent models. Various loss-of-function models, including genetic knockout, pharmacological blockade and selective chemical knockout, confirmed contributions of each constituent activity in vivo. We demonstrate that these individual constituent activities harmonize to govern the overall metabolic efficacy, which predominantly results from synergistic glucagon action to increase energy expenditure, GLP-1 action to reduce caloric intake and improve glucose control, and GIP action to potentiate the incretin effect and buffer against the diabetogenic effect of inherent glucagon activity. These preclinical studies suggest that, so far, this unimolecular, polypharmaceutical strategy has potential to be the most effective pharmacological approach to reversing obesity and related metabolic disorders.

A computer-based method was developed for rapid and automatic identification of potential "frequent hitters". These compounds show up as hits in many different biological assays covering a wide range of targets. A scoring scheme was elaborated from substructure analysis, multivariate linear and nonlinear statistical methods applied to several sets of one and two-dimensional molecular descriptors. The final model is based on a three-layered neural network, yielding a predictive Matthews correlation coefficient of 0.81. This system was able to correctly classify 90% of the test set molecules in a 10-times cross-validation study. The method was applied to database filtering, yielding between 8% (compilation of trade drugs) and 35% (Available Chemicals Directory) potential frequent hitters. This filter will be a valuable tool for the prioritization of compounds from large databases, for compound purchase and biological testing, and for building new virtual libraries.

The quality of electrospray mass spectra of oligonucleotides often suffers from the low signal intensity and clustering with metal ions. This sometimes makes an exact determination of the molecular masses impossible. We describe here several factors contributing to these effects and show some means for their improvement.

Double-stranded oligonucleotides of different lengths and chemical modification have been analyzed by ion spray mass spectrometry. The non-covalent-bonded duplexes can be detected. Therefore, ion spray mass spectrometry is a useful method for investigation of hybridizations of natural and chemically modified oligonucleotides. Since the exact mass of the double strand can be detected, this method can distinguish between specific and nonspecific interaction.

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