Protein-ligand-based pharmacophores: generation and utility assessment in computational ligand profiling.

Ligand profiling is an emerging computational method for predicting the most likely targets of a bioactive compound and therefore anticipating adverse reactions, side effects and drug repurposing. A few encouraging successes have already been reported using ligand 2-D similarity searches and protein-ligand docking. The current study describes the use of receptor-ligand-derived pharmacophore searches as a tool to link ligands to putative targets.

Changes in cancer incidence patterns among a northeastern American Indian population: 1955-1969 versus 1990-2004.

Purpose: This manuscript examines shifts in patterns of cancer incidence among the Seneca Nation of Indians (SNI) for the interval 1955-1969 compared to 1990-2004.

Methods: A retrospective cohort design was used to examine cancer incidence among the SNI during 2 time intervals: 1955-1969 and 1990-2004. Person-years at risk were multiplied by cancer incidence rates for New York State, exclusive of New York City, over 5-year intervals.

Fifty years of cancer in an American Indian population.

Background: A clear understanding of cancer patterns among American Indian tribal groups has been complicated by a variety of issues. A retrospective cohort study design was applied to a Seneca Nation of Indians (SNI) cohort for the period from 1955 through 2004.

Methods: Incident cancers were identified through a computer match with the New York State Cancer Registry.

How large does a compound screening collection need to be?

Increasingly, chemical libraries are being produced which are focused on a biological target or group of related targets, rather than simply being constructed in a combinatorial fashion. A screening collection compiled from such libraries will contain multiple analogues of a number of discrete series of compounds. The question arises as to how many analogues are necessary to represent each series in order to ensure that an active series will be identified.

Chemogenomics: structuring the drug discovery process to gene families.

In the post-genomic era, if all proteins in a gene family can putatively be identified, how can drug discovery effectively tackle so many novel targets that might lack structural and small-molecule inhibitory data? In response, chemogenomics, a new approach that guides drug discovery based on gene families, has been developed. By integrating all information available within a protein family (sequence, SAR data, protein structure), chemogenomics can efficiently enable cross-SAR exploitation, directed compound selection and early identification of optimum selectivity panel members. This review examines recent developments in chemogenomics technologies and illustrates their predictive capabilities with successful examples from two of the major protein families: protein kinases and G-protein-coupled receptors.

Bringing kinases into focus: efficient drug design through the use of chemogenomic toolkits.

The study of protein target families, as opposed to single targets, has become a very powerful tool in chemogenomics-led drug discovery. By integrating comprehensive chemoinformatics and bioinformatics databases with customised analytical tools, a 'Toolkit' approach for the target family is possible, thus allowing predictions of the ligand class, affinity, selectivity and likely off-target issues to be made for the guidance of the medicinal chemist. In this review, we highlight the development and application of the Toolkit approach to the protein kinase superfamily, drawing on examples from lead optimisation studies and the design of focused libraries for lead discovery.

Optimising the EVA descriptor for prediction of biological activity.

EVA is a multivariate molecular descriptor for use in QSAR studies. It is constructed from vibrational eigenvalues derived from either a quantum theoretical or molecular mechanical treatment of molecular structure. This paper applies the method to biological-activity data using measures of the inotropic potential of a range of Calcium channel agonists.

Computational design strategies for combinatorial libraries.

Medicinal chemistry principles are being increasingly applied to the design of smaller, high purity, information-rich libraries. Recent computational advances in statistical methodology, the design of libraries to reduce ADMET problems, targeting protein families and revisiting natural products as sources of inspiration for scaffolds and reagents are all areas of progressive research.