GLARE: a new approach for filtering large reagent lists in combinatorial library design using product properties

We present a novel computer algorithm, called GLARE (Global Library Assessment of REagents), that addresses the issue of optimal reagent selection in combinatorial library design. This program reduces or eliminates the time a medicinal chemist spends examining reagents which a priori cannot be part of a "good" library. Our approach takes the large reagent sets returned by standard chemical database queries and produces often considerably reduced reagent sets that are well-behaved with respect to a specific template. The pruning enforces "goodness" constraints such as the Lipinski rule of five on the product properties such that any reagent selection from the resulting sets produces only "good" products. The algorithm we implemented has three important features: (i) As opposed to genetic algorithms or other stochastic algorithms, GLARE uses a deterministic greedy procedure that smoothly filters out nonviable reagents. (ii) The pruning method can be biased to produce reagent sets with a balanced size, conserving proportionally more reagents in smaller sets. (iii) For very large combinatorial libraries, a partitioning scheme allows libraries as large as 10(12) to be evaluated in 0.25 s on an IBM AMD Opteron processor. This algorithm is validated on a diverse set of 12 libraries. The results that we obtained show an excellent compliance to the product property requirements and very fast timings.     

Microfluidic platform for combinatorial synthesis in picolitre droplets

This paper presents a droplet-based microfluidic platform for miniaturized combinatorial synthesis. As a proof of concept, a library of small molecules for early stage drug screening was produced. We present an efficient strategy for producing a 7 × 3 library of potential thrombin inhibitors that can be utilized for other combinatorial synthesis applications. Picolitre droplets containing the first type of reagent (reagents A(1), A(2), …, A(m)) were formed individually in identical microfluidic chips and then stored off chip with the aid of stabilizing surfactants. These droplets were then mixed to form a library of droplets containing reagents A(1-m), each individually compartmentalized, which was reinjected into a second microfluidic chip and combinatorially fused with picolitre droplets containing the second reagent (reagents B(1), B(2), …, B(n)) that were formed on chip. The concept was demonstrated with a three-component Ugi-type reaction involving an amine (reagents A(1-3)), an aldehyde (reagents B(1-7)), and an isocyanide (held constant), to synthesize a library of small molecules with potential thrombin inhibitory activity. Our technique produced 10(6) droplets of each reaction at a rate of 2.3 kHz. Each droplet had a reaction volume of 3.1 pL, at least six orders of magnitude lower than conventional techniques. The droplets can then be divided into aliquots for different downstream screening applications. In addition to medicinal chemistry applications, this combinatorial droplet-based approach holds great potential for other applications that involve sampling large areas of chemical parameter space with minimal reagent consumption; such an approach could be beneficial when optimizing reaction conditions or performing combinatorial reactions aimed at producing novel materials.     

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.     

清除试剂在液相组合化学中的应用

综述了近年来清除试剂在液相组合化学中的应用,并介绍了一些采用清除试剂对液相化合物库进行分离和纯化的实例。对清除试剂进行了分类,各类清除试剂参与的液相反应以及被清除的非目标产物类型也作了相应的介绍。     

A novel frequency distribution selection method for efficient plate layout of a diverse combinatorial library.

A deterministic method (frequency distribution method) for selecting compounds from a partitioned virtual combinatorial library for efficient synthesis is presented here. The method is based on reagent frequency analysis and can be applied to any library of molecules distributed in any given partitioned chemical space (cluster, cell-based, etc.). Compound selection by reagent frequency distribution can produce a unique, diverse set of molecules that adequately represents the library while requiring the least amount of compounds to be synthesized and minimizing the number of different reagents that must be used. This method also provides a practical solution to the configuration of plate layout. Because the method essentially identifies "expensive" regions in the chemical space to synthesize for a desired diversity or similarity coverage, decisions concerning the necessity to synthesize these compounds can be addressed. Minimum compound generation and efficient plate layout results in savings both in time of synthesis and cost of materials. This method always results in a discrete solution, which can be used for any given library size as well as any combination of reagents and is also readily adaptable to robotic automation.     

Biologically-Inspired Peptide Reagents for Enhancing IMS-MS Analysis of Carbohydrates

The binding properties of a peptidoglycan recognition protein are translated via combinatorial chemistry into short peptides. Non-adjacent histidine, tyrosine, and arginine residues in the protein’s binding cleft that associate specifically with the glycan moiety of a peptidoglycan substrate are incorporated into linear sequences creating a library of 27 candidate tripeptide reagents (three possible residues permutated across three positions). Upon electrospraying the peptide library and carbohydrate mixtures, some noncovalent complexes are observed. The binding efficiencies of the peptides vary according to their amino acid composition as well as the disaccharide linkage and carbohydrate ring-type. In addition to providing a charge-carrier for the carbohydrate, peptide reagents can also be used to differentiate carbohydrate isomers by ion mobility spectrometry. The utility of these peptide reagents as a means of enhancing ion mobility analysis of carbohydrates is illustrated by examining four glucose-containing disaccharide isomers, including a pair that is not resolved by ion mobility alone. The specificity and stoichiometry of the peptide–carbohydrate complexes are also investigated. Trihistidine demonstrates both suitable binding efficiency and successful resolution of disaccharides isomers, suggesting it may be a useful reagent in IMS analyses of carbohydrates.     

Library Design Guided by In-house Developed Computer Tools

In recent years, combinatorial library synthesis for drug discovery begins to migrate from library synthesis solely dictated by chemistry availability to design and synthesis of libraries with more drug-like properties. Lipinski's rule of five has been used to evaluate drug-like properties of individual compound; recently LibProTM, a new computation program has been developed at Pharmacopeia to evaluate durg-like properties of libraries. By using LibPrpTM, chemists at Pharmacopeia are able to obtain information of molecular weight and ClogP distribution of a library, and percentage of library members that violate Lipinski's rule after input structures of synthons for each combinatorial step. Currently, a "virtual library design” approach that is to calculate properties of a library at conceptual phase of the library design has been used to predetermine the value of the library. Also a new computer program used to predict "Absorption” of compounds will also be discussed.     

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.     

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.     

Use of catalyst pharmacophore models for screening of large combinatorial libraries

Using a data set comprised of literature compounds and structure-activity data for cyclin dependent kinase 2, several pharmacophore hypotheses were generated using Catalyst and evaluated using several criteria. The two best were used in retrospective searches of 10 three-dimensional databases containing over 1,000,000 proprietary compounds. The results were then analyzed for the efficiency with which the hypotheses performed in the areas of compound prioritization, library prioritization, and library design. First as a test of their compound prioritization capabilities, the pharmacophore models were used to search combinatorial libraries that were known to contain CDK active compounds to see if the pharmacophore models could selectively choose the active compounds over the inactive compounds. Second as a test of their utility in library design again the pharmacophore models were used to search the active combinatorial libraries to see if the key synthons were over represented in the hits from the pharmacophore searches. Finally as a test of their ability to prioritize combinatorial libraries, several inactive libraries were searched in addition to the active libraries in order to see if the active libraries produced significantly more hits than the inactive libraries. For this study the pharmacophore models showed potential in all three areas. For compound prioritization, one of the models selected active compounds at a rate nearly 11 times that of random compound selection though in other cases models missed the active compounds entirely. For library design, most of the key fragments were over represented in the hits from at least one of the searches though again some key fragments were missed. Finally, for library prioritization, the two active libraries both produced a significant number of hits with both pharmacophore models, whereas none of the eight inactive libraries produced a significant number of hits for both models.     

Analysis and prediction of combinatorial chemistry synthesis and screening data

The goal of combinatorial chemistry is to simultaneously synthesize sets of compounds possessing properties that are then distinguished through screening. As the size of a compound set increases, data analysis becomes more challenging. Analysis of Variance (ANOVA) is an accepted statistical method that offers a straightforward solution to this problem. Two steps encountered by combinatorial scientists appear well suited to ANOVA: the prediction of synthetic outcomes (purity and yield) of set members and the analysis of screening data to identify combinations of reagent inputs that result in molecules with a desired property. To illustrate, a subset of a combinatorial array, referred to as a reaction rehearsal set, is evaluated to create a model predictive of the individual synthetic outcomes of the full matrix. In a second exercise, the biochemical screening data obtained from a combinatorial library is analyzed to identify reagent interactions that result in molecules possessing the sought activity.     

A fast exchange algorithm for designing focused libraries in lead optimization

Combinatorial chemistry is widely used in drug discovery. Once a lead compound has been identified, a series of R-groups and reagents can be selected and combined to generate new potential drugs. The combinatorial nature of this problem leads to chemical libraries containing usually a very large number of virtual compounds, far too large to permit their chemical synthesis. Therefore, one often wants to select a subset of "good" reagents for each R-group of reagents and synthesize all their possible combinations. In this research, one encounters some difficulties. First, the selection of reagents has to be done such that the compounds of the resulting sublibrary simultaneously optimize a series of chemical properties. For each compound, a desirability index, a concept proposed by Harrington,(20) is used to summarize those properties in one fitness value. Then a loss function is used as objective criteria to globally quantify the quality of a sublibrary. Second, there are a huge number of possible sublibraries, and the solutions space has to be explored as fast as possible. The WEALD algorithm proposed in this paper starts with a random solution and iterates by applying exchanges, a simple method proposed by Fedorov(13) and often used in the generation of optimal designs. Those exchanges are guided by a weighting of the reagents adapted recursively as the solutions space is explored. The algorithm is applied on a real database and reveals to converge rapidly. It is compared to results given by two other algorithms presented in the combinatorial chemistry literature: the Ultrafast algorithm of D. Agrafiotis and V. Lobanov and the Piccolo algorithm of W. Zheng et al.     

Multiobjective optimization of combinatorial libraries

Combinatorial chemistry and high-throughput screening have caused a fundamental shift in the way chemists contemplate experiments. Designing a combinatorial library is a controversial art that involves a heterogeneous mix of chemistry, mathematics, economics, experience, and intuition. Although there seems to be little agreement as to what constitutes an ideal library, one thing is certain: only one property or measure seldom defines the quality of the design. In most real-world applications, a good experiment requires the simultaneous optimization of several, often conflicting, design objectives, some of which may be vague and uncertain. In this paper, we discuss a class of algorithms for subset selection rooted in the principles of multiobjective optimization. Our approach is to employ an objective function that encodes all of the desired selection criteria, and then use a simulated annealing or evolutionary approach to identify the optimal (or a nearly optimal) subset from among the vast number of possibilities. Many design criteria can be accommodated, including diversity, similarity to known actives, predicted activity and/or selectivity determined by quantitative structure-activity relationship (QSAR) models or receptor binding models, enforcement of certain property distributions, reagent cost and availability, and many others. The method is robust, convergent, and extensible, offers the user full control over the relative significance of the various objectives in the final design, and permits the simultaneous selection of compounds from multiple libraries in full- or sparse-array format.     

Accelerated screening of phage-display output with alkaline phosphatase fusions

When using multiple targets and libraries, selection of affinity reagents from phage-displayed libraries is a relatively time-consuming process. Herein, we describe an automation-amenable approach to accelerate the process by using alkaline phosphatase (AP) fusion proteins in place of the phage ELISA screening and subsequent confirmation steps with purified protein. After two or three rounds of affinity selection, the open reading frames that encode the affinity selected molecules (i.e., antibody fragments, engineered scaffold proteins, combinatorial peptides) are amplified from the phage or phagemid DNA molecules by PCR and cloned en masse by a Ligation Independent Cloning (LIC) method into a plasmid encoding a highly active variant of E. coli AP. This time-saving process identifies affinity reagents that work out of context of the phage and that can be used in various downstream enzyme linked binding assays. The utility of this approach was demonstrated by analyzing single-chain antibodies (scFvs), engineered fibronectin type III domains (FN3), and combinatorial peptides that were selected for binding to the Epsin N-terminal Homology (ENTH) domain of epsin 1, the c-Src SH3 domain, and the appendage domain of the gamma subunit of the clathrin adaptor complex, AP-1, respectively.     

Virtual darwinian drug design: QSAR inverse problem, virtual combinatorial chemistry, and computational screening

The generation of diversity and its further selection by an external system is a common mechanism for the evolution of the living species and for the current drug design methods. This assumption allows us to label the methods based on generation and selection of molecular diversity as "Darwinian" ones, and to distinguish them from the structure-based, structure-modulation approaches. An example of a Darwinian method is the inverse QSAR. It consists of the computational generation of candidate chemical structures and their selection according to a previously established QSAR model. New trends in the field of combinatorial chemical syntheses comprise the concepts of virtual combinatorial synthesis and virtual or computational screening. Virtual combinatorial synthesis, closely related to inverse QSAR, can be defined as the computational simulation of the generation of new chemical structures by using a combinatorial strategy to generate a virtual library. Virtual screening is the selection of chemical structures having potential desirable properties from a database or virtual library in order to be synthesized and assayed. This review is mainly focused on graph theoretical drug design approaches, but a survey with key references is provided that covers other simulation methods.     

Enantiomeric impurities in chiral catalysts,auxiliaries and synthons used in enantioselective synthesis

Eighty three popular chiral reagents that are used to synthesize a wide variety of compounds of high enantiomeric purity were analyzed in order to determine their enantiomeric composition. Included in the study are chiral catalysts for stereoselective reductions, epoxidations and hydrocarboxylations; chiral auxiliaries including a variety of oxazolidinones; a wide variety of chiral synthons and chiral resolving agents. Enantiomeric impurities were found in all reagents. The reagents were categorized by the level of their enantiomeric contaminants. The four level ranges were: 0.01% to 0.1%, 0.1% to 1%, 1% to 10% and >10%. Over half of the chiral reagents tested had enantiomeric impurities at levels >0.1%. The batch to batch enantiopurity of a reagent from a single source was examined as well as the variation in the enantiopurity of the same reagent from different sources. Possible adverse aspects of having unknown quantities of enantiomeric impurities in stereoselective syntheses are mentioned.     

Mixed oligonucleotides for random mutagenesis: best way of making them

The generation of proteins, especially enzymes, with pre-deliberated, novel properties is a big challenge in the field of protein engineering. This aim, over the years was critically facilitated by newly emerging methods of combinatorial and evolutionary techniques, such as combinatorial gene synthesis followed by functional screening of many structural variants generated in parallel (library). Libraries can be generated by a large number of available methods. Therein the use of mixtures of pre-formed trinucleotide blocks representing codons for the 20 canonical amino acids for oligonucleotide synthesis stands out as allowing fully controlled partial (or total) randomization individually at any number of arbitrarily chosen codon positions of a given gene. This has created substantial demand of fully protected trinucleotide synthons of good reactivity in standard oligonucleotide synthesis. We here review methods for the preparation of oligonucleotide mixtures with a strong focus on codon-specific trinucleotide blocks.