Virtual Combinatorial Library Research Articles

ChemInform Abstract: Computing Wiener‐Type Indices for Virtual Combinatorial Libraries Generated from Heteroatom‐Containing Building Blocks.

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Target-induced formation of neuraminidase inhibitors from in vitro virtual combinatorial libraries

Neuraminidase, a key enzyme responsible for influenza virus propagation, has been used as a template for selective synthesis of small subsets of its own inhibitors from theoretically highly diverse dynamic combinatorial libraries. We show that the library building blocks, aldehydes and amines, form significant amounts of the library components resulting from their coupling by reductive amination only in the presence of the enzyme. The target amplifies the best hits at least 120-fold. The dynamic libraries synthesized and screened in such an in vitro virtual mode form the components that possess high inhibitory activity, as confirmed by enzyme assays with independently synthesized individual compounds.

How to synthesize macrocycles efficiently by using virtual combinatorial libraries.

The selection of different diimines 4 a-c by alkaline earth ions from a virtual combinatorial library (VCL) is described. The products were stabilized by reduction to the diamines 6 a-c; this allowed easy analysis. The library can be directed toward different target molecules 6 a-c upon addition of alkaline earth ions with different radii. Competition experiments show the possibility of synthesizing the macrocycles 6 a, 6 b, and 6 c simultaneously when using Mg(2+), Ca(2+), and Ba(2+) as template ions. The scope of this thermodynamically controlled, reversible approach for macrocycle syntheses is illustrated.

Designing focused libraries using MoSELECT

When designing a combinatorial library it is usually desirable to optimise multiple properties of the library simultaneously and often the properties are in competition with one another. For example, a library that is designed to be focused around a given target molecule should ideally have minimum cost and also contain molecules that are bioavailable. In this paper, we describe the program MoSELECT for multiobjective library design that is based on a multiobjective genetic algorithm (MOGA). MoSELECT searches the product-space of a virtual combinatorial library to generate a family of equivalent solutions where each solution represents a combinatorial subset of the virtual library optimised over multiple objectives. The family of solutions allows the relationships between the objectives to be explored and thus enables the library designer to make an informed choice on an appropriate compromise solution. Experiments are reported where MoSELECT has been applied to the design of various focused libraries.

A probabilistic approach to high throughput drug discovery.

A methodology is presented in which high throughput screening experimental data are used to construct a probabilistic QSAR model which is subsequently used to select building blocks for a virtual combinatorial library. The methodology is based upon statistical probability estimation and not regression. The methodology is applied to the construction of two focused virtual combinatorial libraries: one for cyclic GMP phosphodiesterase type V inhibitors and one for acyl-CoA:cholesterol O-acyltransferase inhibitors. The results suggest that the methodology is capable of selecting combinatorial substituents that lead to active compounds starting with binary (pass/fail) activity measurements.

Scalable methods for the construction and analysis of virtual combinatorial libraries.

One can distinguish between two kinds of virtual combinatorial libraries: viable and accessible . Viable libraries are relatively small in size, are assembled from readily available reagents that have been filtered by the medicinal chemist, and often have a physical counterpart. Conversely, accessible libraries can encompass millions or billions of structures, typically include all possible reagents that are in principle compatible with a particular reaction scheme, and they can never be physically synthesized in their entirety. Although the analysis of viable virtual libraries is relatively straightforward, the handling of large accessible libraries requires methods that scale well with respect to library size. In this work, we present novel, efficient and scalable techniques for the construction, analysis, and in silico screening of massive virtual combinatorial libraries.

Estimation of aqueous solubility of organic compounds with QSPR approach.

To derive a QSPR model for estimation of aqueous solubility of organic compounds. Solubility data for 930 diverse compounds was investigated with principal component regression analysis. This set of compounds consists of pharmaceuticals, pollutants, nutrients, herbicides, and pesticides. The diversity of this collection was analyzed using MACCS fingerprint and BCUT chemistry space. The training set of the solubility data is as diverse as the Available Chemicals Directory, and more diverse than the MDL Drug Data Report. Forty-six molecular descriptors were screened using a genetic algorithm. A QSPR model with a squared correlation coefficient (r2) of 0.92, a root mean square error of 0.53 log molar solubility (log S(w)), an average absolute estimation error of 0.36 log S(w), and a cross-validated q2 of 0.91 was derived. The QSPR model was validated with a test set of 249 compounds not included in the training set. The absolute estimation error for the test set of compounds was 0.39 log S(w). A highly predictive QSPR model for estimating aqueous solubility was derived and validated. This model can be used to estimate aqueous solubility for virtual screening and combinatorial library design.

Design of virtual combinatorial libraries.

AbstractLast year marked the completion of the sequencing effort on the human genome. Advances in the fields of genomics and bioinformatics are widely expected to bring forth a large number of new biochemical targets for drug design by linking specific diseases to single genes or collections thereof. The pharmaceutical and biotechnology sector is now likely to become one of the most active industrial fields in the new century. A dramatic increase in the number of pharmaceutical targets in the near future would create a bottleneck of sorts at the stage of pharmaceutical drug discovery. Historically, this field has used a serial process, screening and optimizing compounds one at a time. However, the advent of combinatorial chemistry and high-throughput screening has significantly altered the face of drug discovery. Rapid generation and screening of combinatorial libraries has become commonplace in both industry and academia; with the ever-growing offering of organic reagents along with the vast palette of organic reactions, the chemical space accessible to combinatorial chemists has dramatically expanded. The application of combinatorial technology, however, is not without a potential caveat—even as the cost of synthesis and testing of a single chemical entity has fallen, it skyrockets when multiplied by the thousands or millions of combinatorial library members. Some of the potential solutions include (i) use of the three-dimensional target structure information in the design process; (ii) application of pharmacophore and quantitative structure-activity relationship (QSAR) methods to focus on libraries that most closely resemble screening hits; and (iii) reduction in the scale on which both medicinal chemistry and screening are currently performed. These solutions imply a need for a rational approach to reducing library size, custom izing the library for a given target, and more efficient in silico screening of a library (or, potentially, multiple libraries) of compounds against a large number of targets of interest. The challenge in accomplishing these goals is in being able to reduce the library while increasing the potential hit rate of individual library members.KeywordsLibrary DesignVirtual LibraryMolar RefractivityLibrary MemberAvailable Chemical DirectoryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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.

Rapid Estimation of Hydrophobicity for Virtual Combinatorial Library Analysis

Novel NPH (Nonpolar, Polar, Hydrogen) descriptors for rapid estimation of hydrophobicity, amenable for filtering extremely large virtual combinatorial libraries (VCL) are proposed, based on atom counts: P_at, the sum of polar atoms (sum of oxygen, nitrogen, phosphorus and sulfur); NP_at, the sum of non-polar atoms (the sum of carbons and halogens minus P_at); SHDA (the sum of hydrogen bond donors and acceptors). In combination with molecular weight, the following related parameters are defined: MWP_at (the “polar” molecular weight); MWNP_at (the “nonpolar” molecular weight); and MWSHDA (the =hydrogen bonding= molecular weight). The NPH descriptors provide moderate-to-good predictive PLS models when external prediction is evaluated against measured logP values (q2 pred > 0.5, n = 7954) or against calculated logP values (q2 pred > 0.6. n=18991). Related to hydrophobicity, the NPH descriptors are intended for fast analyses of extremely large VCLs (106−1012 compounds), even before the enumeration of reactants into products occurs.

A fast algorithm for searching for molecules containing a pharmacophore in very large virtual combinatorial libraries.

We present a new algorithm for identifying molecules that display a pharmacophore, or in general a structural motif, by efficiently constructing and screening huge virtual combinatorial libraries of diverse compounds. The uniqueness of this algorithm is its ability to build and screen libraries of ca. 10(18) 3D molecular conformations within a reasonable time scale, thereby increasing the chemical space that can be virtually screened by many orders of magnitude. The algorithm may be used to design new molecules that display a desired pharmacophore on predefined sets of chemical scaffolds. This is demonstrated herein by screening a library of backbone cyclic peptides to find candidate peptido- and proteinomimetics.

Stochastic similarity selections from large combinatorial libraries

A stochastic procedure for similarity searching in large virtual combinatorial libraries is presented. The method avoids explicit enumeration and calculation of descriptors for every virtual compound, yet provides an optimal or nearly optimal similarity selection in a reasonable time frame. It is based on the principle of probability sampling and the recognition that each reagent is represented in a combinatorial library by multiple products. The method proceeds in three stages. First, a small fraction of the products is selected at random and ranked according to their similarity against the query structure. The top-ranking compounds are then identified and deconvoluted into a list of "preferred" reagents. Finally, all the cross-products of these preferred reagents are enumerated in an exhaustive manner, and systematically compared to the target to obtain the final selection. This procedure has been applied to produce similarity selections from several virtual combinatorial libraries, and the dependency of the quality of the selections on several selection parameters has been analyzed.

Applications of random sampling to virtual screening of combinatorial libraries

We describe statistical techniques for effective evaluation of large virtual combinatorial libraries (>1010 potential compounds). The methods described are used for computationally evaluating templates (prioritization of candidate libraries for synthesis and screening) and for the design of individual combinatorial libraries (e.g., for a given diversity site, reagents can be selected based on the estimated frequency with which they appear in products that pass a computational filter). These statistical methods are powerful because they provide a simple way to estimate the properties of the overall library without explicitly enumerating all of the possible products. In addition, they are fast and simple, and the amount of sampling required to achieve a desired precision is calculable. In this article, we discuss the computational methods that allow random product selection from a combinatorial library and the statistics involved in estimating errors from quantities obtained from such samples. We then describe three examples: (1) an estimate of average molecular weight for the several billion possible products in a four-component Ugi reaction, a quantity that can be calculated exactly for comparison; (2) the prioritization of four templates for combinatorial synthesis using a computational filter based on four-point pharmacophores; and (3) selection of reagents for the four-component Ugi reaction based on their frequency of occurrence in products that pass a pharmacophore filter.

Pharmacophore fingerprinting. 2. Application to primary library design.

A methodology for pharmacophore fingerprinting (PharmPrint), previously described in the context of QSAR, has been used to address the issues involved in primary library design. A subset of the MDDR (MDDR9104) has been used to define a reference set of bioactive molecules. A statistic has been devised to measure the discriminating power of molecular descriptors using the target class assignments for this set, for which the PharmPrint fingerprint outperformed other descriptors. A principal components analysis (PCA) of the fingerprints for the MDDR9104 produces a low dimensional representation within which molecular properties and other libraries can be visualized and explored. PCA calculations on subsets of classes show that this space is robust to the addition of new classes, suggesting that pharmacophoric space is finite and rapidly converging. We demonstrate the application of the PharmPrint methodology to the analysis and design of virtual combinatorial libraries using common scaffolds and building blocks.

Toward designing drug-like libraries: a novel computational approach for prediction of drug feasibility of compounds.

Prediction of the degree of drug-like character in small molecules is of great industrial interest. The major barrier, however, is the lack of a definition for drug-like character. We used the concept of the multilevel chemical compatibility (MLCC) between a compound and a drug library as a measure of the drug-like character of a compound. The rationale is that the local chemical environment of each atom or group of atoms in a compound largely contributes to the stability, toxicity, and metabolism in vivo. A systematic comparison of the local environments within a compound and those within the existing drugs provides a basis for determining whether and how much a compound is drug-like. We applied the MLCC calculations to four test sets: top selling drugs, compounds under biological testing prior to the preclinical test, anticancer drugs, and compounds known to have poor drug-like character. The following conclusions were obtained: (1) A convergent number of unique local structure types were found in the analysis of the library of the existing drugs. It suggests that the current drug library contains about 80% of all the viable types; therefore, discovery of a drug with new local structures is only an event of relatively small probability. (2) The method is highly selective in discerning drug-like compounds: most of the top drugs are predicted to be drug-like, about one-quarter of the biological testing compounds are drug-like, and about one-fifth of the anticancer drugs are drug-like. (3) The method also correctly predicted that none of the known problematic compounds are drug-like. (4) The method is fast enough for computational screening of virtual combinatorial chemistry libraries and databases of available compounds.

Dynamic Combinatorial Chemistry and Virtual Combinatorial Libraries

The reversible connection between sets of basic components allows the generation of a large collection of compounds constituted by all possible combinations that are potentially available. It makes virtual combinatorial libraries of substances accessible through a dynamic approach to combinatorial chemistry. The thermodynamically-driven evolution of the system leads to the actual expression of the constituent(s) that present(s) the best binding to/molecular recognition with the target site (see diagram). The general concept of this methodology has wide applications in a variety of areas of science and technology.

Rational combinatorial library design. 3. Simulated annealing guided evaluation (SAGE) of molecular diversity: a novel computational tool for universal library design and database mining.

We have developed a novel method for molecular diversity sampling called SAGE (simulated annealing guided evaluation of molecular diversity). Compounds in chemical databases or virtual combinatorial libraries are conventionally represented as points in multidimensional descriptor space. The SAGE algorithm selects a desired number of optimally diverse points (compounds) from a database. The diversity of a subset of points is measured by a specially designed diversity function, and the most diverse subset is selected using Simulated Annealing (SA) as the optimization tool. Application of SAGE to two simulated data sets of randomly distributed points in two-dimensional space afforded diverse and representative selection as judged by visual inspection. SAGE was also applied, in comparison with random sampling, to two other simulated data sets with points distributed among many clusters. We found that SAGE sampling covered significantly more clusters than the random sampling. By defining a fraction of data points as active, we also compared SAGE with random sampling in terms of hit rates. We showed that when the percentage of active points was low, the hit rates obtained by SAGE were always higher than those obtained by random sampling. When the percentage of active points was high, the performance of SAGE, in terms of individual hit rates, depended upon the data structure. However, in all cases, SAGE performed better than random sampling when cluster hit rates were used as the criterion.

DNA Aptamers Selected Against the HIV-1trans-Activation-responsive RNA Element Form RNA-DNA Kissing Complexes

In vitro selection was performed in a DNA library, made of oligonucleotides with a 30-nucleotide random sequence, to identify ligands of the human immunodeficiency virus type-1 trans-activation-responsive (TAR) RNA element. Aptamers, extracted after 15 rounds of selection-amplification, either from a classical library of sequences or from virtual combinatorial libraries, displayed an imperfect stem-loop structure and presented a consensus motif 5'ACTCCCAT in the apical loop. The six central bases of the consensus were complementary to the TAR apical region, giving rise to the formation of RNA-DNA kissing complexes, without disrupting the secondary structure of TAR. The RNA-DNA kissing complex was a poor substrate for Escherichia coli RNase H, likely due to steric and conformational constraints of the DNA/RNA heteroduplex. 2'-O-Methyl derivatives of a selected aptamer were binders of lower efficiency than the parent aptamer in contrast to regular sense/antisense hybrids, indicating that the RNA/DNA loop-loop region adopted a non-canonical heteroduplex structure. These results, which allowed the identification of a new type of complex, DNA-RNA kissing complex, demonstrate the interest of in vitro selection for identifying non-antisense oligonucleotide ligands of RNA structures that are of potential value for artificially modulating gene expression.

A New Approach to Design Virtual Combinatorial Library with Genetic Algorithm Based on 3D Grid Property

Combinatorial chemistry is a type of synthetic strategy which systematically connects a set of “building blocks” of varying structures to yield a large array of diverse molecular entities. Since the molecular diversity of combinatorial library used for the two stages of drug discovery process which are exploring lead compounds and performing structural modification on lead compounds is different, choosing appropriate fragments as the building blocks becomes a critical step for combinatorial chemistry. The number of available chemical fragments is often so great that it is impossible to use all of the fragments in one synthetic experiment. An effective method is much needed to select building blocks from the enormous chemical fragments to synthesize a combinatorial library with desired molecular diversity. In this article, we proposed a novel idea to define molecular diversity based on three-dimensional (3D) grid force field properties including electric potential and steric potential parameters and demonstrated how to apply genetic algorithm to choose a subset of fragment for the construction of a virtual combinatorial library. In the first example, we tried to construct a virtual library for benzodiazepine derivatives with maximum molecular diversity. In the second example, a virtual library is constructed for structural modification of (−)-huperzine A based on the 3D grid properties. By analyzing fragments frequency in the target library, we can understand which fragments are appropriate as building blocks. Furthermore, we can predict the biological activity of library compounds with 3D-QSAR model or by evaluating the binding energy of library compounds with the target receptors sites.

World Wide Web-based system for the calculation of substituent parameters and substituent similarity searches

Easy to use, interactive, and platform-independent WWW-based tools are ideal for development of chemical applications. By using the newly emerging Web technologies such as Java applets and sophisticated scripting, it is possible to deliver powerful molecular processing capabilities directly to the desk of synthetic organic chemists. In Novartis Crop Protection in Basel, a Web-based molecular modelling system has been in use since 1995. In this article two new modules of this system are presented: a program for interactive calculation of important hydrophobic, electronic, and steric properties of organic substituents, and a module for substituent similarity searches enabling the identification of bioisosteric functional groups. Various possible applications of calculated substituent parameters are also discussed, including automatic design of molecules with the desired properties and creation of targeted virtual combinatorial libraries.