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Gerald M Maggiora

Publications and source records attributed to Gerald M Maggiora.

8 recordsLinked to original sources
PubMed2006

Hierarchical strategy for identifying active chemotype classes in compound databases.

A general methodology is presented for analyzing patterns of activity in compound databases, which is based on the use of structural chemotypes and provides a focused, hierarchical classification of active compounds. Each node in the hierarchical tree corresponds to a specific chemotype and is labeled by a unique code or identifier. All chemotypes at a given level of the hierarchy define equivalence classes, and those of higher structural resolution have a strict parent-child (i.e. subset) relationship to those of lower resolution. Active chemotypes contain a relatively high proportion of actives and are characterized through the use of enrichment plots. These plots show the relationship of occupancy to activity enrichment for a set of chemotypes at a given level of structural resolution. Paths through the hierarchy from chemotypes of lower to those of higher structural resolution (e.g. reduced cyclic system skeletons --> cyclic system skeletons --> cyclic systems --> complete molecules) are unique. Unique paths in the hierarchy that only pass through active chemotypes are called chains or paths of actives. These chains provide links for identifying structurally related active compounds at increasing levels of structural resolution. Analysis of actives can also be carried out at any specific level of structural resolution deemed appropriate by the investigator. Chemotype codes can be used to search compound databases for new molecules possessing these codes or sets of hierarchically related codes. An example, based on the NCI AIDS database, is presented that illustrates the general approach and provides a more detailed description of several interesting classes of active chemotypes and their inter-relationships.

Chemistry Techniques, Analytical↗
PubMed2005

Evaluating the high-throughput screening computations.

The judges evaluated the submissions for the McMaster University High-Throughput Data-Mining and Docking Competition based on 3 criteria: identification of active compounds, percent enrichment, and overview of the competition. Using these metrics, 4 of the participating groups found meaningful enrichment, and 3 groups made perceptive comments about the general nature of the competition.

Computational Biology↗
PubMed2005

Evaluating molecular similarity using reduced representations of the electron density.

A model system of four benzodiazepine-like ligands for the central benzodiazepine receptors (CBRs) and peripheral benzodiazepine receptors (PBRs)is examined using a genetic algorithm procedure (GAGS) designed for evaluating molecular similarity. The method is based on the alignment of reduced representations generated from the critical points of the electron density computed at medium crystallographic resolution. The results are further characterized by a comparison with alignments produced by MIMIC, a field-based superimposition method that matches both steric and electrostatic molecular fields. The alignments produced by the two methods are generally seen to be consistent. The relationships of the compounds' binding affinities for both CBRs and PBRs to the alignments determined by GAGS yield a set of structural features required for significant binding to benzodiazepine receptors. Benefits of using reduced representations for evaluating molecular similarities and for constructing pharmacophore models are discussed.

Benzodiazepines↗
PubMed2005

Hit-directed nearest-neighbor searching.

This work describes a practical strategy used at Pharmacia for identifying compounds for follow-up screening following an initial HTS campaign against targets where no 3-D structural information is available and preliminary SAR models do not exist. The approach explicitly takes into account different representations of chemistry space and identifies compounds for follow-up screening that are likely to provide the best overall coverage of the chemistry spaces considered. Specifically, the work employs hit-directed nearest-neighbor (HDNN) searching of compound databases based upon a set of "probe compounds" obtained as hits in the preliminary high-throughput screens. Four different molecular representations that generate nearly unique chemistry spaces are used. The representations include 3-D, 2-D, 2-D topological BCUTs (2-DT) and molecular fingerprints derived from substructural fragments. In the case of the BCUT representations the NN searching is distance based, while in the case of molecular fingerprints a similarity-based measure is used. Generally, the results obtained differ significantly among all four methods, that is, the sets of NN compounds have surprisingly little overlap. Moreover, in all of the four chemistry space representations, a minimum of 3- to 4-fold enrichment in actives over random screening is observed even though the actives identified in each of the sets of NNs are in large measure unique. These results suggest that use of multiple searches based upon a variety of molecular representations provides an effective way of identifying more hits in HDNN searches of chemistry spaces than can be realized with single searches.

Bacteria↗
PubMed2004

Assessment of the consistency of medicinal chemists in reviewing sets of compounds.

Medicinal chemists are frequently asked to review lists of compounds to assess their drug- or leadlike nature and to evaluate the suitability of lead compounds based on their "attractiveness" and/or synthetic feasibility as a basis for launching a drug-discovery campaign. It is often felt that one medicinal chemist's opinion is as good as any other, but is it? In an attempt to answer this question, an experiment was performed in conjunction with a recent compound acquisition program (CAP) conducted at Pharmacia. Historically, the CAP included a review of many thousands of compounds by medicinal chemists who eliminate anything deemed undesirable for any reason. In a review conducted in 2002, about 22 000 compounds requiring review by medicinal chemists were broken down into 11 lists of approximately 2000 compounds each. Unknown to the medicinal chemists, a subset of 250 compounds, previously rejected by a very experienced senior medicinal chemist, was added to each of the lists. Most of the 13 medicinal chemists who participated in this process reviewed two lists, although some only reviewed a single list and one reviewed three lists. Those compounds that were deemed unacceptable were recorded and tabulated in various ways to assess the consistency of the reviews. It was found that medicinal chemists were not very consistent in the compounds they rejected as being undesirable. The inconsistency arises from the subjective analysis that all humans utilize when considering "data sets" of any kind. This has important implications for pharmaceutical project teams where individual medicinal chemists review lists of primary screening hits to identify those compounds suitable for follow-up. Once a compound is removed from a list, it and other structurally similar compounds are effectively removed from further consideration. This can also have an impact on computational chemists who are developing models for assessing the desirability or attractiveness of different classes of compounds for lead discovery.

Chemistry, Pharmaceutical↗
PubMed2004

Molecular similarity measures.

Molecular similarity is a pervasive concept in chemistry. It is essential to many aspects of chemical reasoning and analysis and is perhaps the fundamental assumption underlying medicinal chemistry. Dissimilarity, the complement of similarity, also plays a major role in a growing number of applications of molecular diversity in combinatorial chemistry, high-throughput screening, and related fields. How molecular information is represented, called the representation problem, is important to the type of molecular similarity analysis (MSA) that can be carried out in any given situation. In this work, four types of mathematical structure are used to represent molecular information: sets, graphs, vectors, and functions. Molecular similarity is a pairwise relationship that induces structure into sets of molecules, giving rise to the concept of a chemistry space. Although all three concepts molecular similarity, molecular representation, and chemistry space are treated in this chapter, the emphasis is on molecular similarity measures. Similarity measures, also called similarity coefficients or indices, are functions that map pairs of compatible molecular representations, that is, representations of the same mathematical form, into real numbers usually, but not always, lying on the unit interval. This chapter presents a somewhat pedagogical discussion of many types of molecular similarity measures, their strengths and limitations, and their relationship to one another.

Mathematics↗
PubMedDate not supplied

Data shaving: a focused screening approach.

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.

Journal Article↗