A novel approach to combinatorial library design

We address the problem of designing a general-purpose combinatorial library to screen for pharmaceutical leads. Conventional approaches focus on diversity as the primary factor in designing such libraries. We suggest making screening libraries out of a set of pharmaceutically relevant scaffolds, with multiple analogs per scaffold. The rationale for this rests on the fact that even though the hit-rate in active series is much higher than in the database as a whole, often a large fraction of the compounds in active series are inactive. This is especially true when the series has not been optimized for the target under study. We introduce the concept of hit-rate within a series and use historic screening data to arrive at a crude estimate for it. We then use simple probability arguments to show that 50-100 compounds are required in each series in order to be nearly certain of finding at least one active compound in each true active series for any given target.     

CombiDOCK: Structure-based combinatorial docking and library design

We have developed a strategy for efficiently docking a large combinatorial library into a target receptor. For each scaffold orientation, all potential fragments are attached to the scaffold, their interactions with the receptor are individually scored and factorial combinations of fragments are constructed. To test its effectiveness, this approach is compared to two simple control algorithms. Our method is more efficient than the controls at selecting best scoring molecules and at selecting fragments for the construction of an exhaustive combinatorial library. We also carried out a retrospective analysis of the experimental results of a 10×10×10 exhaustive combinatorial library. An enrichment factor of approximately 4 was found for identifying the compounds in the library that are active at 330 nM.     

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.     

A scalable approach to combinatorial library design for drug discovery

In this paper, we propose an algorithm for the design of lead generation libraries required in combinatorial drug discovery. This algorithm addresses simultaneously the two key criteria of diversity and representativeness of compounds in the resulting library and is computationally efficient when applied to a large class of lead generation design problems. At the same time, additional constraints on experimental resources are also incorporated in the framework presented in this paper. A computationally efficient scalable algorithm is developed, where the ability of the deterministic annealing algorithm to identify clusters is exploited to truncate computations over the entire data set to computations over individual clusters. An analysis of this algorithm quantifies the tradeoff between the error due to truncation and computational effort. Results applied on test data sets corroborate the analysis and show improvement by factors as large as 10 or more, depending on the data sets.     

Computational methods and software in computer-aided combinatorial library design

Recent trends in the computer-aided design of diverse and focussed combinatorial libraries are surveyed. First, chemical data input, storage and retrieval including chemical database management and virtual chemical structure enumeration are outlined as background. Then, the optimization of ADMET parameters, diversity maximization, molecular similarity search, QSAR-based virtual screening, pharmacophore search and molecular docking are discussed.     

Rational principles of compound selection for combinatorial library design

It is practically impossible in a short period of time to synthesize and test all compounds in any large exhaustive chemical library. We discuss rational approaches to selecting representative subsets of virtual libraries that help direct experimental synthetic efforts for both targeted and diverse library design. For targeted library design, we consider principles based on the similarity to lead molecules. In the case of diverse library design, we discuss algorithms aimed at the selection of both diverse and representative subsets of the entire chemical library space. We illustrate methodologies with several practical examples.     

Application of self-organizing maps in compounds pattern recognition and combinatorial library design

In the computer-aided drug design, in order to find some new leads from a large library of compounds, the pattern recognition study of the diversity and similarity assessment of the chemical compounds is required; meanwhile in the combinatorial library design, more attention is given to design target focusing library along with diversity and drug-likeness criteria. This review presents the current state-of-art applications of Kohonen self-organizing maps (SOM) for studying the compounds pattern recognition, comparing the property of molecular surfaces, distinguishing drug-like and nondrug-like molecules, splitting a dataset into the proper training and test sets before constructing a QSAR (Quantitative Structural-Activity Relationship) model, and also for the combinatorial libraries comparison and the combinatorial library design. The Kohonen self-organizing map will continue to play an important role in drug discovery and library design.     

Scaffold diversity of natural products: inspiration for combinatorial library design

Natural products contain scaffold structures that can be systematically exploited for the design of combinatorial compound libraries with druglike properties. We review approaches for scaffold identification, and compare properties and pharmacophoric features of drugs and natural products. In particular, an application of the self-organizing map technique is presented for natural product-derived compound and library design.     

Reagent Selector: using Synthon Analysis to visualize reagent properties and assist in combinatorial library design

Reagent Selector is an intranet-based tool that aids in the selection of reagents for use in combinatorial library construction. The user selects an appropriate reagent group as a query, for example, primary amines, and further refines it on the basis of various physicochemical properties, resulting in a list of potential reagents. The results of this selection process are, in turn, converted into synthons: the fragments or R-groups that are to be incorporated into the combinatorial library. The Synthon Analysis interface graphically depicts the chemical properties for each synthon as a function of the topological bond distance from the scaffold attachment point. Displayed in this fashion, the user is able to visualize the property space for the universe of synthons as well as that of the synthons selected. Ultimately, the reagent list that embodies the selected synthons is made available to the user for reagent procurement. Application of the approach to a sample reagent list for a G-protein coupled receptor targeted library is described.     

Chemoinformatic analysis of a supertargeted combinatorial library of styryl molecules

Styryl dyes are fluorescent, lipophilic cations that have been used as specific labeling probes of mitochondria in living cells. For specific applications such as epifluorescence microscopy or flow cytometry, it is often desirable to synthesize fluorescent derivatives with optimized excitation, emission, and localization properties. Here, we present a chemoinformatic strategy suitable for multiparameter analysis of a combinatorial library of styryl molecules supertargeted to mitochondria. The strategy is based on a simple additive model relating the spectral and subcellular localization characteristics of styryl compounds to the two chemical building blocks that are used to synthesize the molecules. Using a cross-validation approach, the additive model predicts with a high degree of confidence the subcellular localization and spectral properties of the styryl product, from numerical scores that are independently associated with the individual building blocks of the molecule. The fit of the data indicates that more complex, nonadditive interactions between the two building blocks play a minor role in determining the molecule's optical or biological properties. Moreover, the observed additive relationship allows mechanistic inferences to be made regarding the structure-property relationship observed for this particular class of molecules. It points to testable, mechanistic hypotheses about how chemical structure, fluorescence, and localization properties are related.     

Focused combinatorial library design based on structural diversity, druglikeness and binding affinity score

The advent of focused library and virtual screening has reduced the disadvantage of combinatorial chemistry and changed it to a realizable and cost-effective tool in drug discovery. Usually, genetic algorithms (GAs) are used to quickly finding high-scoring molecules by sampling a small subset of the total combinatorial space. Therefore, scoring functions play essential roles in focused library design. Reported here is our initial attempt to establish a new approach for generating a target-focused library using the combination of the scores of structural diversity and binding affinity with our newly improved drug-likeness scoring functions. Meanwhile, a software package, named LD1.0, was developed on the basis of the new approach. One test on a cyclooxygenase (COX)2-focused library successfully reproduced the structures that have been experimentally studied as COX2-selective inhibitors. Another test is on a peroxisome proliferator-activated receptors gamma-focused library design, which not only reproduces the key fragments in the approved (thiazolidinedione) TZD drugs, but also generates some new structures that are more active than the approved drugs or published ligands. Both of the two tests took approximately 15% of the running time of the ordinary molecular docking method. Thus, our new approach is an effective, reliable, and practical way for building up a properly sized focused library with a high hit rate, novel structure, and good ADME/T profile.     

Structure-based combinatorial library design: discovery of non-peptidic inhibitors of caspases 3 and 8

Structure-based design of a combinatorial array was carried out in order to identify non-peptidic thiomethylketone inhibitors of caspases 3 and 8. Five compounds from the designed array were active against caspase 3, and two were active against caspase 8. A 2.5-Å resolution co-crystal structure of caspase 3 and a thiomethylketone array member is reported. The structure-based design strategy has proved useful for identifying caspase inhibitors.     

From solid-phase synthesis of pi-conjugated oligomers to combinatorial library construction and screening

A feature article describing concepts for the solid-phase synthesis of pi-conjugated oligomers as material-related structures and the translation of the synthetic routes into combinatorial protocols.     

Recent advances in microwave-assisted combinatorial synthesis and library generation

Progress and developments made in microwave-assisted combinatorial synthesis and library production since 2002 are reviewed. The use of microwave technology in both solution and solid phase synthesis is discussed with special reference to agrochemical applications where appropriate.     

Efficient solution phase combinatorial access to a library of pyrazole- and triazole-containing compounds

An efficient method for the synthesis of multidentate molecules, with various building blocks, such as pyrazolyl, triazolyl, pyridyl, furyl, or bispinidyl derivatives, is reported. This approach is based on the condensation of easily available N-alkyl heteroarylamines with N-hydroxymethyl pyrazoles or triazoles.     

On the suitability of different representations of solid catalysts for combinatorial library design by genetic algorithms

Genetic algorithms are widely used to solve and optimize combinatorial problems and are more often applied for library design in combinatorial chemistry. Because of their flexibility, however, their implementation can be challenging. In this study, the influence of the representation of solid catalysts on the performance of genetic algorithms was systematically investigated on the basis of a new, constrained, multiobjective, combinatorial test problem with properties common to problems in combinatorial materials science. Constraints were satisfied by penalty functions, repair algorithms, or special representations. The tests were performed using three state-of-the-art evolutionary multiobjective algorithms by performing 100 optimization runs for each algorithm and test case. Experimental data obtained during the optimization of a noble metal-free solid catalyst system active in the selective catalytic reduction of nitric oxide with propene was used to build up a predictive model to validate the results of the theoretical test problem. A significant influence of the representation on the optimization performance was observed. Binary encodings were found to be the preferred encoding in most of the cases, and depending on the experimental test unit, repair algorithms or penalty functions performed best.     

The use of FTICR-MS to detect chemical tags from a combinatorial library

The use of tagging in combinatorial chemistry permits tracking of the solid phase as it is taken through iterative split and mix cycles. Several analytical approaches to the identification of tags (and hence the chemical history of the support) have been described. We describe herein a novel chemical tagging strategy for combinatorial solid phase chemistry. The identities of the tags attached to a single bead are discovered by the high resolution, accurate mass technique of Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS).