WONKA: objective novel complex analysis for ensembles of protein-ligand structures.

WONKA is a tool for the systematic analysis of an ensemble of protein-ligand structures. It makes the identification of conserved and unusual features within such an ensemble straightforward. WONKA uses an intuitive workflow to process structural co-ordinates. Ligand and protein features are summarised and then presented within an interactive web application. WONKA's power in consolidating and summarising large amounts of data is described through the analysis of three bromodomain datasets. Furthermore, and in contrast to many current methods, WONKA relates analysis to individual ligands, from which we find unusual and erroneous binding modes. Finally the use of WONKA as an annotation tool to share observations about structures is demonstrated. WONKA is freely available to download and install locally or can be used online at http://wonka.sgc.ox.ac.uk.

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.

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.

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.

Prediction of biological activity for high-throughput screening using binary kernel discrimination.

High-throughput screening has made a significant impact on drug discovery, but there is an acknowledged need for quantitative methods to analyze screening results and predict the activity of further compounds. In this paper we introduce one such method, binary kernel discrimination, and investigate its performance on two datasets; the first is a set of 1650 monoamine oxidase inhibitors, and the second a set of 101 437 compounds from an in-house enzyme assay. We compare the performance of binary kernel discrimination with a simple procedure which we call "merged similarity search", and also with a feedforward neural network. Binary kernel discrimination is shown to perform robustly with varying quantities of training data and also in the presence of noisy data. We conclude by highlighting the importance of the judicious use of general pattern recognition techniques for compound selection.

Where are the GaPs? A rational approach to monomer acquisition and selection.

Gridding and partitioning (GaP) is a computational method for the classification and selection of monomers for combinatorial libraries. The molecules are described in terms of the pharmacophoric groups they contain and where those pharmacophoric groups can be located in three-dimensional space. The approach involves a detailed conformational analysis of each molecule. This conformational analysis is done within a common coordinate frame, thus enabling the monomers to be compared. The use of a partitioned space is central to this particular application as it facilitates the identification of regions of space which are not well represented by existing compounds. Several ways to extend the use of partitioned pharmacophore spaces are described. Applications of the approach in monomer acquisition and in library design are outlined.

PLUMS: a program for the rapid optimization of focused libraries.

PLUMS is a new method to perform rational monomer selection for combinatorial chemistry libraries. The algorithm has been developed to optimize focused libraries with specific two-dimensional and/or three-dimensional properties. A preliminary step is the identification of those molecules in the initial virtual library which satisfy the imposed property constraints; we define these molecules as the virtual hits. From the virtual hits, PLUMS generates a starting library, which is the true combinatorial library that includes all the virtual hits. Monomers are then removed in an iterative fashion, thus reducing the size of the library. At each iteration, the worst monomer is removed. Each sublibrary is selected using a global scoring function, which balances effectiveness and efficiency. The iterative process continues until one is left with a library that consists entirely of virtual hits. The optimal library, which is the best compromise between effectiveness and efficiency, can then be selected according to the score. During the iterative process, equivalent solutions may well occur and are taken into account by the algorithm, according to a user-defined parameter. The number of monomers for each substitution site and the size of the library are parameters that can be either optimized or used to constrain the selection. The results obtained on two test libraries are presented. PLUMS was compared with genetic algorithms (GA) and monomer frequency analysis (MFA), which are widely used for monomer selection. For the two test libraries, PLUMS and GA gave equivalent results. MFA is the fastest method, but it can give misleading solutions. Possible advantages and disadvantages of the different methods are discussed.

Implementation of a system for reagent selection and library enumeration, profiling, and design.

We describe an integrated suite of computational tools which are used to assist in the selection of compounds for biological assays and the design of combinatorial libraries. These functions are delivered in a platform-independent manner via a corporate intranet and are used by computational experts and nonexperts alike. While the system was primarily designed to be used prior to synthesis, it can also be used to provide structural information for library registration and for decoding beads in tagged libraries. We describe a simple statistical method for monomer selection and compare it to computationally more demanding approaches.

Biaryl acids: novel non-nucleoside inhibitors of HIV reverse transcriptase types 1 and 2.

A series of biaryl acids has been found to show micromolar inhibition of the HIV reverse transcriptase (RT) from types 1 and 2 with IC50S in the micromolar range. The series was discovered by consideration of the polymerase active site and sub-structure searching of the company compound collection. Synthesis of analogues to investigate the SAR is described. Two of these compounds have shown inhibition of HIV-2 RT only.

Benzophenone derivatives: a novel series of potent and selective inhibitors of human immunodeficiency virus type 1 reverse transcriptase.

A series of benzophenone derivatives has been synthesized and evaluated as inhibitors of HIV-1 reverse transcriptase (RT) and the growth of HIV-1 in MT-4 cells. Through the use of the structure-activity relationships within this series of compounds and computational chemistry techniques, a binding conformation is proposed. The SAR also indicated that the major interactions of 1h with the RT enzyme are through hydrogen bonding of the amide and benzophenone carbonyls and pi-orbital interactions with the benzophenone nucleus and an aromatic function separated from the benzophenone by a suitable spacer group. The crystal structure of compound 1h has been determined. A number of compounds with potent inhibitory activity against HIV-1 RT and HIV in cellular assays at levels comparable with AZT and our efforts to identify a metabolically stable analogue are described.

Synthesis and anti-HIV-1 activity of a series of imidazo[1,5-b]pyridazines.

A series of substituted imidazo[1,5-b]pyridazines have been prepared and tested for inhibitory activity against the reverse transcriptase of HIV-1 (RT) and their ability to inhibit the growth of infected MT-4 cells. Crystal data are reported on two compounds, 15c and 33. From the structure-activity relationships developed within this and other series, it is proposed that key features of the interaction with RT include hydrogen-bond acceptor and aromatic pi-orbital bonding with the imidazopyridazine nucleus and a benzoyl function separated from the heterocycle by a suitable spacer group. Exceptional activity against the reverse transcriptase of HIV-1 (IC50 = 0.65 nM) was obtained with a 2-imidazolyl-substituted derivative, 7-[2-(1H-imidazol-1- yl)-5-methylimidazo-[1,5-b]pyridazin-7-yl]-1-phenyl-1-heptanone (33) which is attributed to additional binding of the imidazole sp2 nitrogen atom. A number of the compounds in this series also inhibit the replication of HIV-1 in vitro in MT-4 and C8166 cells at levels observed with the nucleoside AZT.