A very large diversity space of synthetically accessible compounds for use with drug design programs

We have constructed a very large virtual diversity space containing more than 10¹³ chemical compounds. The diversity space is built from about 400 combinatorial libraries, which have been expanded by choosing sizeable collections of suitable R-groups that can be attached to each link point of their scaffolds. These R-group collections have been created by selecting reagents that have drug-like properties from catalogs of available chemicals. As members of known combinatorial libraries, the compounds in the diversity space are in general synthetically accessible and useful as potential drug leads. Hence, the diversity space can be used as a vast source of compounds by a de novo drug design program. For example, we have used such a program to generate inhibitors of HIV integrase enzyme that exhibited activity in the micromolar range.     

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.     

OptiDock: virtual HTS of combinatorial libraries by efficient sampling of binding modes in product space

Products from combinatorial libraries generally share a common core structure that can be exploited to improve the efficiency of virtual high-throughput screening (vHTS). In general, it is more efficient to find a method that scales with the total number of reagents (Sigma growth) rather with the number of products (Pi growth). The OptiDock methodology described herein entails selecting a diverse but representative subset of compounds that span the structural space encompassed by the full library. These compounds are docked individually using the FlexX program (Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. J. Mol. Biol. 1995, 251, 470-489) to define distinct docking modes in terms of reference placements for combinatorial core atoms. Thereafter, substituents in R-cores (consisting of the core structure substituted at a single variation site) are docked, keeping the core atoms fixed at the coordinates dictated by each reference placement. Interaction energies are calculated for each docked R-core with respect to the target protein, and energies for whole compounds are calculated by finding the reference core placement for which the sum of corresponding R-core energies is most negative. The use of diverse whole compounds to define binding modes is a key advantage of the protocol over other combinatorial docking programs. As a result, OptiDock returns better-scoring conformers than does serially applied FlexX. OptiDock is also better able to find a viable docked pose for each library member than are other combinatorial approaches.     

Integrating virtual screening and combinatorial chemistry for accelerated drug discovery

Virtual screening is increasingly being used in drug discovery programs with a growing number of successful applications. Experimental methodologies developed to speed up the drug discovery processes include high-throughput screening and combinatorial chemistry. The complementarities between computational and experimental screenings have been recognized and reviewed in the literature. Computational methods have also been used in the combinatorial chemistry field, in particular in library design. However, the integration of computational and combinatorial chemistry screenings has been attempted only recently. Combinatorial libraries (experimental or virtual) represent a notable source of chemically related compounds. Advances in combinatorial chemistry and deconvolution strategies, have enabled the rapid exploration of novel and dense regions in the chemical space. The present review is focused on the integration of virtual and experimental screening of combinatorial libraries. Applications of virtual screening to discover novel anticancer agents and our ongoing efforts towards the integration of virtual screening and combinatorial chemistry are also discussed.     

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.     

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.     

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.     

Library fingerprints: a novel approach to the screening of virtual libraries

We propose a novel method to prioritize libraries for combinatorial synthesis and high-throughput screening that assesses the viability of a particular library on the basis of the aggregate physical-chemical properties of the compounds using a na?ve Bayesian classifier. This approach prioritizes collections of related compounds according to the aggregate values of their physical-chemical parameters in contrast to single-compound screening. The method is also shown to be useful in screening existing noncombinatorial libraries when the compounds in these libraries have been previously clustered according to their molecular graphs. We show that the method used here is comparable or superior to the single-compound virtual screening of combinatorial libraries and noncombinatorial libraries and is superior to the pairwise Tanimoto similarity searching of a collection of combinatorial libraries.     

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.     

The dark side of disulfide-based dynamic combinatorial chemistry

During the last two decades, disulfide-based dynamic combinatorial chemistry has been extensively used in the field of molecular recognition to deliver artificial receptors for molecules of biological interest. Commonly, the nature of library members and their relative amounts are provided from HPLC-MS analysis of the libraries, allowing the identification of potential binders for a target (bio)molecule. By re-investigating dynamic combinatorial libraries generated from a simple 2,5-dicarboxy-1,4-dithiophenol building block in water, we herein demonstrated that multiple analytical tools were actually necessary in order to comprehensively describe the libraries in terms of size, stereochemistry, affinity, selectivity, and finally to get a true grasp on the different phenomena at work within dynamic combinatorial systems.

We show that multiple analytical tools are necessary in order to describe the different phenomena within disulfide-based dynamic combinatorial libraries in terms of size, stereochemistry, affinity and selectivity.     

Immunoassays: biological tools for high throughput screening and characterisation of combinatorial libraries

In the demanding field of proteomics, there is an urgent need for affinity-catcher molecules to implement effective and high throughput methods for analysing the human proteome or parts of it. Antibodies have an essential role in this endeavour, and selection, isolation and characterisation of specific antibodies represent a key issue to meet success. Alternatively, it is expected that new, well-characterised affinity reagents generated in rapid and cost-effective manners will also be used to facilitate the deciphering of the function, location and interactions of the high number of encoded protein products. Combinatorial approaches combined with high throughput screening (HTS) technologies have become essential for the generation and identification of robust affinity reagents from biological combinatorial libraries and the lead discovery of active/mimic molecules in large chemical libraries. Phage and yeast display provide the means for engineering a multitude of antibody-like molecules against any desired antigen. The construction of peptide libraries is commonly used for the identification and characterisation of ligand-receptor specific interactions, and the search for novel ligands for protein purification. Further improvement of chemical and biological resistance of affinity ligands encouraged the "intelligent" design and synthesis of chemical libraries of low-molecular-weight bio-inspired mimic compounds. No matter what the ligand source, selection and characterisation of leads is a most relevant task. Immunological assays, in microtiter plates, biosensors or microarrays, are a biological tool of inestimable value for the iterative screening of combinatorial ligand libraries for tailored specificities, and improved affinities. Particularly, enzyme-linked immunosorbent assays are frequently the method of choice in a large number of screening strategies, for both biological and chemical 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.     

Assessing the scaffold diversity of screening libraries

Medicinal chemists have traditionally realized assessments of chemical diversity and subsequent compound acquisition, although a recent study suggests that experts are usually inconsistent in reviewing large data sets. To analyze the scaffold diversity of commercially available screening collections, we have developed a general workflow aimed at (1) identifying druglike compounds, (2) clustering them by maximum common substructures (scaffolds), (3) measuring the scaffold diversity encoded by each screening collection independently of its size, and finally (4) merging all common substructures in a nonredundant scaffold library that can easily be browsed by structural and topological queries. Starting from 2.4 million compounds out of 12 commercial sources, four categories of libraries could be identified: large- and medium-sized combinatorial libraries (low scaffold diversity), diverse libraries (medium diversity, medium size), and highly diverse libraries (high diversity, low size). The chemical space covered by the scaffold library can be searched to prioritize scaffold-focused libraries.     

Constructing virtual combinatorial fragment libraries based upon MDL Drug Data Report database

Structural analysis of known drugs or drug-like compounds provides important information for drug design. The 142553 drug molecules in the MDL Drug Data Report database were analyzed, and then the common structural features were extracted. According to the common structural features, drug molecules were segmented into 32017 fragments, including 13642 ring fragments, 10076 linker fragments, and 8299 side chain fragments. These fragments were further used to establish three types of virtual combinatorial fragment libraries: a basic framework library containing 13574 rings; a linker library of 8051 linkers and a pharmacophore library of 34244 fragments combined by rings and side chains. After energy minimization, all fragments in the above three libraries maintain reasonable geometrical features and spatial conformations, and would be useful for building a virtual combinatorial database and de novo drug design.     

Functionality‐Independent DNA Encoding of Complex Natural Products

DNA encoded chemical libraries (DELs) link the powers of genetics and chemical synthesis via combinatorial optimization. Through combinatorial chemistry, DELs can grow to the unprecedented size of billions to trillions. To take full advantage of the DEL approach, linking the power of genetics directly to chemical structures would offer even greater diversity in a finite chemical world. Natural products have evolved an incredible structural diversity along with their biological evolution. Herein, we used traditional Chinese medicines (TCMs) as examples in a late‐stage modification toolbox approach to annotate these complex organic compounds with amplifiable DNA barcodes, which could be easily incorporated into a DEL. The method of end‐products labeling also generates a cluster of isomers with a single DNA tag at different sites. These isomers provide an additional spatial diversity for multiple accessible pockets of targeted proteins. Notably, a novel PARP1 inhibitor from TCM has been identified from the natural products enriched DEL (nDEL).