Design and synthesis of a maximally diverse and druglike screening library using REM resin methodology

Application of parallel artificial membrane permeability assay technique and chemometric modeling for blood-brain barrier permeability prediction of protein kinase inhibitors

Aim: This study aims to investigate the passive diffusion of protein kinase inhibitors through the blood-brain barrier (BBB) and to develop a model for their permeability prediction. Materials & methods: We used the parallel artificial membrane permeability assay to obtain logPe values of each of 34 compounds and calculated descriptors for these structures to perform quantitative structure-property relationship modeling, creating different regression models. Results: The logPe values have been calculated for all 34 compounds. Support vector machine regression was considered the most reliable, and CATS2D_09_DA, CATS2D_04_AA, B04[N-S] and F07[C-N] descriptors were identified as the most influential to passive BBB permeability. Conclusion: The quantitative structure-property relationship-support vector machine regression model that has been generated can serve as an efficient method for preliminary screening of BBB permeability of new analogs.

Historical overview of chemical library design

High-throughput chemistry (HTC) is approaching its 20-year anniversary. Since 1992, some 5,000 chemical libraries, prepared for the purpose of biological investigation and drug discovery, have been published in the scientific literature. This review highlights the key events in the history of HTC with emphasis on library design. A historical perspective on the design of screening, targeted, and optimization libraries and their application is presented. Design strategies pioneered in the 1990s remain viable in the twenty-first century.

Blood-brain barrier permeability considerations for CNS-targeted compound library design

A further refinement of the concept of drug-likeness is required for compound libraries intended for central nervous system (CNS) targets to account for the limitations imposed by blood-brain barrier permeability. This review describes criteria and processes that can be applied in the de novo design and assembly of libraries to increase the odds of compounds residing within CNS-accessible chemical space. A number of published examples where CNS activity and/or penetration characteristics have been a factor in library design are discussed.

Trimethylsilyl-directed 1,3-dipolar cycloaddition reactions in the solid-phase synthesis of 1,2,3-triazoles

[reaction: see text] A regioselective method for the preparation of 1,5-trisubstituted 1H-1,2,3-triazoles via a 1,3-dipolar cycloaddition of 1-trimethylsilylacetylenes with organoazides is described. Immobilization of the azide on REM resin and subsequent cycloaddition afforded a 2 x 2 x 4 x 3 membered 1,5-disubstituted 1H-1,2,3-triazole library with an average purified yield of 68%.

REALISIS: A Medicinal Chemistry-Oriented Reagent Selection, Library Design, and Profiling Platform

REALISIS is a software system for reagent selection, library design, and profiling, developed to fit the workflow of bench chemists and medicinal chemists. Designed to be portable, the software offers a comprehensive graphical user interface and rapid, integrated functionalities required for reagent retrieval and filtering, product enumeration, and library profiling. REALISIS is component-based, consisting of four main modules: reagent searching; reagent filtering; library enumeration; and library profiling. Each module allows the chemist to access specific functionalities and diverse filtering and profiling mechanisms. By implementing the entire process of reagent selection, library design, and profiling and by integrating all the necessary functionalities for this process, REALISIS cuts the time required to design combinatorial and noncombinatorial libraries from several days to a few hours.

Chemical microarrays, fragment diversity, label-free imaging by plasmon resonance--a chemical genomics approach

Chemical genomics aim to create synergy between synthetic small molecule chemistry and biosciences employing genomic tools and information. Central to chemical genomics is the discovery of bioactive compounds from novel targets for pharmaceutical lead development. The field is challenged both by the multitude and novelty of protein and other biomacromolecular targets to be studied. Affinity fingerprints, data sets of binding interactions between collections of chemicals and their macromolecular receptors, hold promise to guide drug design and study protein function for groups of related compounds and families of biomacromolecules. Despite their fundamental relevance, neither experimental protocols nor databases of quantitative and comprehensive description of binding interactions for small molecule ligands and biomacromolecular receptors are available. Chemical microarrays in combination with label-free imaging provide a novel route towards the systematic and standardized acquisition and application of such affinity fingerprint information.

The solid-phase synthesis and use of N-monosubstituted piperazines in chemical library synthesis

An efficient solid-phase synthesis of mono-N-substituted piperazines is presented. The key transformation involves a selective borane amide bond reduction in the presence of a carbamate resin linkage. This synthetic route takes advantage of the large diverse pool of commercially available carboxylic acids, acid chlorides, and sulfonyl chlorides. The solid-phase approach facilitates parallel processing by eliminating the need for column chromatography after each synthetic step. The N-monosubstituted piperazines were shown to react with polymeric activated tetrafluorophenol (TFP) reagents to generate arrays of amides and sulfonamides in good purity for biological testing.

OptDesign: extending optimizable k-dissimilarity selection to combinatorial library design

Optimizable k-dissimilarity (OptiSim) selection entails drawing a series of subsamples of size k from a population and choosing the "best" candidate from each such subsample for inclusion in the selection set. By varying the size of the subsample, one can control the balance between representativeness and diversity in the selection set obtained. In the original formulation, a uniform random sampling from among valid candidates was used to draw the subsamples from a single target population. Here we describe in detail two key modifications that serve to extend the OptiSim methodology to vector selection for interdependent variables, specifically as applied to the design of combinatorial sublibraries. The first modification involves pivoting between variables: subsamples are drawn from each reagent pool in turn, with the viability of each candidate being evaluated in isolation as well as in terms of the products it will produce from complementary reagents already selected. The filters applied may be static or dynamic in nature, with molecular weight and hydrophobicity being examples of the former and structural diversity with respect to reagents already selected being an example of the latter. The second key modification is adding the ability to bias the selection of candidate reagents for inclusion in the subsamples. Taken together, these modifications support the efficient generation of multiblock and other sparse matrix designs that are both representative and diverse, and for which "backfilling" of designs edited to remove undesirable reagents or products is straightforward. The method is intrinsically fast and efficient, since enumeration of the full combinatorial is not required- only those candidates actually considered for inclusion need be evaluated. Moreover, because the subsample selection step is separate from the diversity-based selection of the "best" candidate, incorporating such bias in favor of a competing criterion such as low price provides a "natural," nonparametric mechanism for generating designs that are likely to be "good" in a double-objective, Pareto sense.

Selection criteria for drug-like compounds

The fast identification of quality lead compounds in the pharmaceutical industry through a combination of high throughput synthesis and screening has become more challenging in recent years. Although the number of available compounds for high throughput screening (HTS) has dramatically increased, large-scale random combinatorial libraries have contributed proportionally less to identify novel leads for drug discovery projects. Therefore, the concept of 'drug-likeness' of compound selections has become a focus in recent years. In parallel, the low success rate of converting lead compounds into drugs often due to unfavorable pharmacokinetic parameters has sparked a renewed interest in understanding more clearly what makes a compound drug-like. Various approaches have been devised to address the drug-likeness of molecules employing retrospective analyses of known drug collections as well as attempting to capture 'chemical wisdom' in algorithms. For example, simple property counting schemes, machine learning methods, regression models, and clustering methods have been employed to distinguish between drugs and non-drugs. Here we review computational techniques to address the drug-likeness of compound selections and offer an outlook for the further development of the field.