Efficient substructure searching of large chemical libraries: the ABCD chemical cartridge.

Efficient substructure searching is a key requirement for any chemical information management system. In this paper, we describe the substructure search capabilities of ABCD, an integrated drug discovery informatics platform developed at Johnson & Johnson Pharmaceutical Research & Development, L.L.

Advanced biological and chemical discovery (ABCD): centralizing discovery knowledge in an inherently decentralized world.

We present ABCD, an integrated drug discovery informatics platform developed at Johnson & Johnson Pharmaceutical Research & Development, L.L.C.

Conformational sampling of bioactive molecules: a comparative study.

The necessity to generate conformations that sample the entire conformational space accessible to a given molecule is ubiquitous in the field of computer-aided drug design. Protein-ligand docking, 3D database searching, and 3D QSAR are three commonly used techniques that depend critically upon the quality and diversity of the generated conformers. Although there are a wide range of conformational search algorithms available, the extent to which they sample conformational space is often unclear.

A distance geometry heuristic for expanding the range of geometries sampled during conformational search.

A recent study of crystal structures of protein-ligand complexes has shown that bioactive conformations tend to be more extended than random ones (Diller and Merz, J. Comput. Aid.

Enzyme classification by ligand binding.

The problem of assigning a biochemical function to newly discovered proteins has been traditionally approached by expert enzymological analysis, sequence analysis, and structural modeling. In recent years, the appearance of databases containing protein-ligand interaction data for large numbers of protein classes and chemical compounds have provided new ways of investigating proteins for which the biochemical function is not completely understood. In this work, we introduce a method that utilizes ligand-binding data for functional classification of enzymes.

A method for quantifying and visualizing the diversity of QSAR models.

Feature selection is one of the most commonly used and reliable methods for deriving predictive quantitative structure-activity relationships (QSAR). Many feature selection algorithms are stochastic in nature and often produce different solutions depending on the initialization conditions. Because some features may be highly correlated, models that are based on different sets of descriptors may capture essentially the same information, however, such models are difficult to recognize.

Conformational sampling by self-organization.

A new stochastic algorithm for conformational sampling is described. The algorithm generates molecular conformations that are consistent with a set of geometric constraints, which include interatomic distance bounds and chiral volumes derived from the molecular connectivity table. The algorithm repeatedly selects individual geometric constraints at random and updates the respective atomic coordinates toward satisfying the chosen constraint.