No abstract

The number of compounds available for evaluation as part of the drug discovery process continues to increase. These compounds may exist physically or be stored electronically allowing screening by either actual or virtual means. This growing number of compounds has generated an increasing need for effective strategies to direct screening efforts. Initial efforts toward this goal led to the development of methods to select diverse sets of compounds for screening, methods to cluster actives into related groups of compounds, and tools to select compounds similar to actives of interest for further screening. In this work we extend these earlier efforts to exploit information about inactive compounds to help make rational decisions about which sets of compounds to include as part of a continuing screening campaign, or as part of a focused follow-up effort. This method uses the information from inactive compounds to "shave" off or deprioritize compounds similar to inactives from further consideration. This methodology can be used in two ways: first, to provide a rational means of deciding when sufficient compounds containing certain structural features have been tested and second as a tool to enhance similarity searching around known actives. Similarity searching is improved by deprioritizing compounds predicted to be inactive, due to the presence of structural features associated with inactivity.

The objective of this chapter is to summarize and evaluate some of the most common chemoinformatic methods that are applied to the analysis of high-throughput-screening data. The chapter will briefly describe current high-throughput-screening practices and will stress how the major constraint on the application of chemoinformatics is often the quality of high-throughput-screening data. Discussion of the NCI dataset and how it differs from most high-throughput-screening datasets will be made to highlight this point.

The application of maximum likelihood multivariate calibration methods to the fluorescence emission spectra of mixtures of acenaphthylene, naphthalene, and phenanthrene in acetonitrile is described. Maximum likelihood principal components regression (MLPCR) takes into account the measurement error structure in the spectral data in constructing the calibration model. Measurement errors for the fluorescence spectra are shown to exhibit both a heteroscedastic and correlated noise structure. MLPCR is compared with principal components regression (PCR) and partial least-squares regression (PLS). The application of MLPCR reduces the prediction errors by about a factor of two over PCR and PLS when a pooled estimate of the measurement error covariance matrix is employed. However, when only the heteroscedascity is incorporated into MLPCR, no improvement in results is observed, indicating the importance of accounting for correlated measurement errors.

We report a novel synthetic cysteine oxidase consisting of a ferrocene-beta-cyclodextrin conjugate in which the ferrocene moiety is bound to the secondary hydroxyl side of the cyclodextrin cavity through an ethylenediamine linker. Cysteine oxidation occurs after the ferrocene group is electrochemically oxidized to the ferricinium form, and this generates a voltammetric electrocatalytic wave, the magnitude of which is related to the rate constant for cysteine oxidation. Comparison of cysteine oxidation rates for the primary and secondary beta-cyclodextrin derivatives (105 and 1470 M-1 s-1, respectively) shows that the secondary derivatives are more effective synthetic enzymes. Substrate selectivity of the secondary derivative is demonstrated by comparison of oxidation rates for cysteine (1470 M-1 s-1) and glutathione (260 M-1 s-1) at pH 7.0. The rate constant for cysteine oxidation was 3-fold higher at pH 8.0. With a constant synthetic enzyme concentration, electrocatalytic limiting currents increased linearly with increasing cysteine concentration to a maximum at 6 mM cysteine; above this concentration, the current decreased significantly. These and other results suggest that product inhibition of the catalytic cycle occurs as a result of cystine binding more strongly to the cyclodextrin than cysteine.