Biomedical subjects

D V S Green

Publications and source records attributed to D V S Green.

3 recordsLinked to original sources

PubMed2004

Design of a compound screening collection for use in high throughput screening.

In this paper we introduce a quantitative model that relates chemical structural similarity to biological activity, and in particular to the activity of lead series of compounds in high-throughput assays. From this model we derive the optimal screening collection make up for a given fixed size of screening collection, and identify the conditions under which a diverse collection of compounds or a collection focusing on particular regions of chemical space are appropriate strategies. We derive from the model a diversity function that may be used to assess compounds for acquisition or libraries for combinatorial synthesis by their ability to complement an existing screening collection. The diversity function is linked directly through the model to the goal of more frequent discovery of lead series from high-throughput screening. We show how the model may also be used to derive relationships between collection size and probabilities of lead discovery in high-throughput screening, and to guide the judicious application of structural filters.

Chemistry, Pharmaceutical↗

PubMed2004

Methods for library design and optimisation.

The introduction of combinatorial chemistry groups into pharmaceutical companies provoked a desire for efficient and effective methods for library design and optimisation. This, in turn, has resulted in a large number of scientific publications, detailing a variety of approaches to the problem. This review attempts to describe the major works in the literature, to set them in context both chronologically and scientifically, and to identify the outstanding challenges that must be addressed, if this area of research is to maintain the rapid progress seen hitherto.

Combinatorial Chemistry Techniques↗

PubMedDate not supplied

The reduced graph descriptor in virtual screening and data-driven clustering of high-throughput screening data.

Virtual screening and high-throughput screening are two major components of lead discovery within the pharmaceutical industry. In this paper we describe improvements to previously published methods for similarity searching with reduced graphs, with a particular focus on ligand-based virtual screening, and describe a novel use of reduced graphs in the clustering of high-throughput screening data. Literature methods for reduced graph similarity searching encode the reduced graphs as binary fingerprints, which has a number of issues. In this paper we extend the definition of the reduced graph to include positively and negatively ionizable groups and introduce a new method for measuring the similarity of reduced graphs based on a weighted edit distance. Moving beyond simple similarity searching, we show how more flexible queries can be built using reduced graphs and describe a database system that allows iterative querying with multiple representations. Reduced graphs capture many important features of ligand-receptor interactions and, in conjunction with other whole molecule descriptors, provide an informative way to review HTS data. We describe a novel use of reduced graphs in this context, introducing a method we have termed data-driven clustering, that identifies clusters of molecules represented by a particular whole molecule descriptor and enriched in active compounds.

Journal Article↗