Optimizing the size and configuration of combinatorial libraries

This paper addresses a major issue in library design, namely how to efficiently optimize the library size (number of products) and configuration (number of reagents at each position) simultaneously with other properties such as diversity, cost, and drug-like physicochemical property profiles. These objectives are often in competition, for example, minimizing the number of reactants while simultaneously maximizing diversity, and thus present difficulties for traditional optimization methods such as genetic algorithms and simulated annealing. Here, a multiobjective genetic algorithm (MOGA) is used to vary library size and configuration simultaneously with other library properties. The result is a family of solutions that explores the tradeoffs in the objectives. This is achieved without the need to assign relative weights to the objectives. The user is then able to make an informed choice on an appropriate compromise solution. The method has been applied to two different virtual libraries: a two-component aminothiazole library and a four-component benzodiazepine library.     

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.     

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.     

The Application of Combinatorial Chemistry in Catalysis

In recent few years combinatorial methodology has been extensively used in material science research. Based on the desired properties of materials, various high throughput synthesizing and screening technologies were developed. These high throughput technologies can increase our speed to more than hundred folds for finding and optimizing materials. One of the most active areas is catalysis. Scientists are developing novel high throughput technologies to screen catalyst libraries to find and optimize new catalysts for chemical industry. In this area die key is combinatorial catalytic reactor design, catalyst library synthesis, and product detection. Systematic technologies for catalyst library synthesis and characterization were developed in our laboratory. In this work, catalyst in situ synthesis, parallel reactor design, and detection methods will be introduced. Combining with the powerful combinatorial methodology, good chemistry design will make our work even more efficient. Hence, as an example of combining combinatorial technologies with chemistry design, a successful catalyst design is also introduced.     

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.     

Current progress in natural product-like libraries for discovery screening

Natural product-like libraries represent an effort to combine the attractive features of natural products and combinatorial libraries for high-throughput screening. Three approaches to natural product-like library design are discussed: (1) Libraries based on core scaffolds from individual natural products, (2) libraries of diverse structures with general structural characteristics of natural products, and (3) libraries of diverse structures based on specific structural motifs from classes of natural products. Examples of successful applications in discovery screening are described for each category. These studies highlight the exciting potential of natural product-like libraries in both chemical biology and drug discovery.     

Solution-phase parallel synthesis of hexahydro-1H-isoindolone libraries via tactical combination of Cu-catalyzed three-component coupling and Diels-Alder reactions

Parallel solution-phase synthesis of combinatorial libraries of hexahydro-1 H-isoindolones exploiting a novel "tactical combination" of Cu-catalyzed three-component coupling and Diels-Alder reactions was accomplished. Three distinct libraries consisting of 24 members (library I), 60 members (library II), and 32 members (library III) were constructed. Variation of three substituents on the isoindolone scaffold in library I was exclusively achieved by the choice of the building blocks. In the syntheses of libraries II and III, sublibraries of isoindolone scaffolds were prepared initially in a one-pot/two-step process and were further diversified via Pd-catalyzed Suzuki cross-coupling reaction with boronic acids at two different diversification points. The Lipinski profiles and calculated ADME properties of the compounds are also reported.     

A general method for designing combinatorial peptide libraries decodable by amino acid analysis

Herein we describe an algorithm for designing combinatorial peptide libraries for split-and-mix synthesis on solid support that are decodable by amino acid analysis (AAA) of the beads. AAA is a standard service analysis available in most biochemical laboratories, and it allows one to control the quality of the peptide on each bead, an important feature that is missing from most library decoding protocols. In the algorithm, each AA is assigned to two variable positions in the sequence grouped in a "unique pair". This arrangement limits sequence design because both the number of unique pairs U (setting the maximum number of variable AA) and the maximum number S of different AA per variable position depend on the peptide length N (U=N(N-1)/2), S=N-1). The method is therefore only suitable for focused libraries. An application example is shown for the selection of peptides with N-terminal proline or hydroxyproline catalyzing an aldol reaction from a combinatorial library of 65536 octapeptides. A simple enumeration program is available to help design combinatorial libraries decodable by amino acid analysis. The method applies to linear and cyclic peptides, can be used for nonnatural building blocks, including beta-amino acids, and should help to explore the vast chemistry of linear and cyclic peptide for catalysis and bioactivity.     

Desirability-based methods of multiobjective optimization and ranking for global QSAR studies. Filtering safe and potent drug candidates from combinatorial libraries

Up to now, very few applications of multiobjective optimization (MOOP) techniques to quantitative structure-activity relationship (QSAR) studies have been reported in the literature. However, none of them report the optimization of objectives related directly to the final pharmaceutical profile of a drug. In this paper, a MOOP method based on Derringer's desirability function that allows conducting global QSAR studies, simultaneously considering the potency, bioavailability, and safety of a set of drug candidates, is introduced. The results of the desirability-based MOOP (the levels of the predictor variables concurrently producing the best possible compromise between the properties determining an optimal drug candidate) are used for the implementation of a ranking method that is also based on the application of desirability functions. This method allows ranking drug candidates with unknown pharmaceutical properties from combinatorial libraries according to the degree of similarity with the previously determined optimal candidate. Application of this method will make it possible to filter the most promising drug candidates of a library (the best-ranked candidates), which should have the best pharmaceutical profile (the best compromise between potency, safety and bioavailability). In addition, a validation method of the ranking process, as well as a quantitative measure of the quality of a ranking, the ranking quality index (Psi), is proposed. The usefulness of the desirability-based methods of MOOP and ranking is demonstrated by its application to a library of 95 fluoroquinolones, reporting their gram-negative antibacterial activity and mammalian cell cytotoxicity. Finally, the combined use of the desirability-based methods of MOOP and ranking proposed here seems to be a valuable tool for rational drug discovery and development.     

Beyond mere diversity: tailoring combinatorial libraries for drug discovery

Combinatorial library design attempts to choose the best set of substituents for a combinatorial synthetic scheme to maximize the chances of finding a useful compound, such as a drug lead. Initial efforts were focused primarily on maximizing diversity, perhaps allowing some bias by the inclusion of a small, fixed set of pharmacophoric substituents. However, many factors besides diversity impact good library design for drug discovery. A library can be better "tailored" by assigning the candidate substituents to categories such as polar, pharmacophoric, rigid, low molecular weight, and expensive. Stratified sampling by successive steps of D-optimal design generates diverse designs which are also consistent with desirable profiles of these properties. Comparing the diversity scores among design profiles reveals the tradeoffs between diversity, physical property distributions, synthetic difficulty, expense, and pharmacophoric bias. The diversity scores can be calibrated by scoring the best designs from subsets of the candidates made either from specific classes of substituents or by randomly eliminating candidates. This procedure shows how poor random designs are compared even to highly biased optimal designs. Library design requires a synergistic effort between computational and synthetic medicinal chemists, so specialized interactive software has been developed to integrate substructure searching, display, and statistical experimental design to facilitate this interaction for the effective design of well-tailored libraries.     

Scaffold architecture and pharmacophoric properties of natural products and trade drugs: application in the design of natural product-based combinatorial libraries

Natural products were analyzed to determine whether they contain appealing novel scaffold architectures for potential use in combinatorial chemistry. Ring systems were extracted and clustered on the basis of structural similarity. Several such potential scaffolds for combinatorial chemistry were identified that are not present in current trade drugs. For one of these scaffolds a virtual combinatorial library was generated. Pharmacophoric properties of natural products, trade drugs, and the virtual combinatorial library were assessed using a self-organizing map. Obviously, current trade drugs and natural products have several topological pharmacophore patterns in common. These features can be systematically explored with selected combinatorial libraries based on a combination of natural product-derived and synthetic molecular building blocks.     

基于结构的组合库设计策略在新药创制中的应用

合理设计一些容量小的、针对特定靶标的化合物库(称为集中库,focused library),是当前组合库设计中的热点。当靶标的三维结构可以通过X射线衍射或NMR等手段确定后,人们就能采用几种不同的策略来进行组合库的设计。本文讨论了在靶标结合位点的约束下,进行组合库设计的主要方法及程序,同时强调了它们的优点与不足。通过这些方法的成功应用实例,显示了它们在新药创制中的广泛应用前景。     

Luddite: an information-theoretic library design tool

We present an algorithm for the design of either combinatorial or discrete informative libraries. This approach is based on information theoretic techniques used extensively in coding theory. We have extended the information theoretic formalism to include an arbitrary number of property distribution constraints, such as Lipinski "drug-like" distributions. The method is demonstrated by comparing and contrasting a variety of different libraries selected from a single combinatorial source pool of compounds.     

Sensitivity analysis and other improvements to tailored combinatorial library design

"Tailoring" combinatorial libraries was developed several years ago as a very general and intuitive method to design diverse compound collections while controlling the profile of other pharmaceutically relevant properties. The candidate substituents were assigned to "categorical bins" according to their properties, and successive steps of D-optimal design were performed to generate diverse substituent sets consistent with required membership quotas from each bin. This serial algorithm was expedient to implement from existing D-optimal design codes, but was order-dependent and did not generally locate the very best possible design. A new "parallel" Fedorov search algorithm has now been implemented that can find the most diverse property-tailored design. An ambiguous mass penalty has been added, whereby most duplicate masses can be eliminated with little loss of library diversity. Sensitivity analysis has also been added to quantitatively explore the diversity trade-offs due to increasing or decreasing each specific kind of bias.     

Surrogate docking: structure-based virtual screening at high throughput speed

Summary Structure-based screening using fully flexible docking is still too slow for large molecular libraries. High quality docking of a million molecule library can take days even on a cluster with hundreds of CPUs. This performance issue prohibits the use of fully flexible docking in the design of large combinatorial libraries. We have developed a fast structure-based screening method, which utilizes docking of a limited number of compounds to build a 2D QSAR model used to rapidly score the rest of the database. We compare here a model based on radial basis functions and a Bayesian categorization model. The number of compounds that need to be actually docked depends on the number of docking hits found. In our case studies reasonable quality models are built after docking of the number of molecules containing 50 docking hits. The rest of the library is screened by the QSAR model. Optionally a fraction of the QSAR-prioritized library can be docked in order to find the true docking hits. The quality of the model only depends on the training set size – not on the size of the library to be screened. Therefore, for larger libraries the method yields higher gain in speed no change in performance. Prioritizing a large library with these models provides a significant enrichment with docking hits: it attains the values of 13 and 35 at the beginning of the score-sorted libraries in our two case studies: screening of the NCI collection and a combinatorial libraries on CDK2 kinase structure. With such enrichments, only a fraction of the database must actually be docked to find many of the true hits. The throughput of the method allows its use in screening of large compound collections and in the design of large combinatorial libraries. The strategy proposed has an important effect on efficiency but does not affect retrieval of actives, the latter being determined by the quality of the docking method itself. Electronic supplementary material is available at http://dx.doi.org/10.1007/s10822-005-9002-6.     

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.