Combinatorial Chemistry and Molecular Diversity. An Overview

Overview of high-throughput screening

High-throughput screening (HTS) is a key process used in drug discovery to identify hits from compound libraries that may become leads for medicinal chemistry optimization. This updated overview discusses the utilization of compound libraries, compounds derived from combinatorial and parallel synthesis campaigns and natural product sources; creation of mother and daughter plates; and compound storage, handling, and bar coding in HTS. The unit also presents an overview of established and emerging assay technologies (i.e., time-resolved fluorescence, fluorescence polarization, fluorescence-correlation spectroscopy, functional whole cell assays, and high-content assays) and their integration in automation hardware and IT systems. This revised unit provides updated descriptions of state-of-the-art instrumentation and technologies in this rapidly changing environment. The section on assay methodologies now also covers enzyme complementation assays and methods for high-throughput screening of ion channel activities. Finally, a section on criteria for assay robustness is included discussing the Z'-factor, which is now a widely accepted criterion for evaluation and validation of high throughput screening assays.

A combinatorial approach to biochemical space: description and application to the redox distribution of metabolism

Redox chemistry is central to life on Earth. It is well known that life uses redox chemistry to capture energy from environmental chemical energy gradients. Here, we propose that a second use of redox chemistry, related to building biomass from environmental carbon, is equally important to life. We apply a method based on chemical structure to evaluate the redox range of different groups of terrestrial biochemicals, and find that they are consistently of intermediate redox range. We hypothesize the common intermediate range is related to the chemical space required for the selection of a consistent set of metabolites. We apply a computational method to show that the redox range of the chemical space shows the same restricted redox range as the biochemicals that are selected from that space. By contrast, the carbon from which life is composed is available in the environment only as fully oxidized or reduced species. We therefore argue that redox chemistry is essential to life for assembling biochemicals for biomass building. This biomass-building reason for life to require redox chemistry is in addition (and in contrast) to life's use of redox chemistry to capture energy. Life's use of redox chemistry for biomass capture will generate chemical by-products-that is, biosignature gases-that are not in redox equilibrium with life's environment. These potential biosignature gases may differ from energy-capture redox biosignatures.

Molecular similarity and diversity in chemoinformatics: from theory to applications

This review is dedicated to a survey on molecular similarity and diversity. Key findings reported in recent investigations are selectively highlighted and summarized. Even if this overview is mainly centered in chemoinformatics, applications in other areas (pharmaceutical and medical chemistry, combinatorial chemistry, chemical databases management, etc.) are also introduced. The approaches used to define and describe the concepts of molecular similarity and diversity in the context of chemoinformatics are discussed in the first part of this review. We introduce, in the second and third parts, the descriptions and analyses of different methods and techniques. Finally, current applications and problems are enumerated and discussed in the last part.

Conformational analysis by intersection: CONAN

As high throughput techniques in chemical synthesis and screening improve, more demands are placed on computer assisted design and virtual screening. Many of these computational methods require one or more three-dimensional conformations for molecules, creating a demand for a conformational analysis tool that can rapidly and robustly cover the low-energy conformational spaces of small molecules. A new algorithm of intersection is presented here, which quickly generates (on average <0.5 seconds/stereoisomer) a complete description of the low energy conformational space of a small molecule. The molecule is first decomposed into nonoverlapping nodes N (usually rings) and overlapping paths P with conformations (N and P) generated in an offline process. In a second step the node and path data are combined to form distinct conformers of the molecule. Finally, heuristics are applied after intersection to generate a small representative collection of conformations that span the conformational space. In a study of approximately 97,000 randomly selected molecules from the MDDR, results are presented that explore these conformations and their ability to cover low-energy conformational space.

The recent impact of solid-phase synthesis on medicinally relevant benzoannelated nitrogen heterocycles

Benzoannelated heterocycles such as benzodiazepines and indoles can be prepared efficiently through cyclization on solid supports, although no single approach is currently universal for the preparation of all benzoannelated N-heterocycle chemistries. In this review, a number of synthetic strategies for the generation of benzoannelated nitrogen heterocycles using resin-bound substrates have been described. Classical heterocycle forming reactions such as the Fischer indole, the Bischler-Napieralski tetrahydroisoquinoline, the Pictet-Spengler tetrahydro-beta-carboline, the Tsuge, the Nenitzescu and the Richter cinnoline reaction are presented. In addition, the Heck, Sonogashira, Wittig, Diels-Alder, and olefin metathesis reactions have been also used. Multicomponent reactions such as the Grieco three-component assembly have been exploited for the synthesis of heterocycles. Cyclative cleavage from the solid support is particularly suitable for the synthesis of heterocycles while particular emphasis has been focused on the synthesis of libraries and the use of combinatorial chemistry techniques. In addition, the most relevant pharmacological properties of benzoannelated nitrogen heterocycles are included.

Life in the fast lane: high-throughput chemistry for lead generation and optimisation

The pharmaceutical industry has come under increasing pressure due to regulatory restrictions on the marketing and pricing of drugs, competition, and the escalating costs of developing new drugs. These forces can be addressed by the identification of novel targets, reductions in the development time of new drugs, and increased productivity. Emphasis has been placed on identifying and validating new targets and on lead generation: the response from industry has been very evident in genomics and high throughput screening, where new technologies have been applied, usually coupled with a high degree of automation. The combination of numerous new potential biological targets and the ability to screen large numbers of compounds against many of these targets has generated the need for large diverse compound collections. To address this requirement, high-throughput chemistry has become an integral part of the drug discovery process.

Chemical database techniques in drug discovery

Chemical databases are becoming a powerful tool in drug discovery. Database searches based on possible requirements for biological activity can identify compounds that might be suitable for further analysis or indicate novel ways to achieve the desired activity. What considerations are involved in the construction and searching of chemical databases?

Diversity and coverage of structural sublibraries selected using the SAGE and SCA algorithms

It is often impractical to synthesize and test all compounds in a large exhaustive chemical library. Herein, we discuss rational approaches to selecting representative subsets of virtual libraries that help direct experimental synthetic efforts for diverse library design. We compare the performance of two stochastic sampling algorithms, Simulating Annealing Guided Evaluation (SAGE; Zheng, W.; Cho, S. J.; Waller, C. L.; Tropsha, A. J. Chem. Inf. Comput. Sci. 1999, 39, 738-746.) and Stochastic Cluster Analysis (SCA; Reynolds, C. H.; Druker, R.; Pfahler, L. B. Lead Discovery Using Stochastic Cluster Analysis (SCA): A New Method for Clustering Structurally Similar Compounds J. Chem. Inf. Comput. Sci. 1998, 38, 305-312.) for their ability to select both diverse and representative subsets of the entire chemical library space. The SAGE and SCA algorithms were compared using u- and s-optimal metrics as an independent assessment of diversity and coverage. This comparison showed that both algorithms were capable of generating sublibraries in descriptor space that are diverse and give reasonable coverage (i.e. are representative) of the original full library. Tests were carried out using simulated two-dimensional data sets and a 27 000 compound proprietary structural library as represented by computed Molconn-Z descriptors. One of the key observations from this work is that the algorithmically simple SCA method is capable of selecting subsets that are comparable to the more computationally intensive SAGE method.

Database mining applied to central nervous system (CNS) activity

A data set of 389 compounds, active in the central nervous system (CNS) and divided into eight classes according to the receptor type, was extracted from the RBI database and analyzed by Self-Organizing Maps (SOM), also known as Kohonen Artificial Neural Networks. This method gives a 2D representation of the distribution of the compounds in the hyperspace derived from their molecular descriptors. As SOM belongs to the category of unsupervised techniques, it has to be combined with another method in order to generate classification models with predictive ability. The fuzzy clustering (FC) approach seems to be particularly suitable to delineate clusters in a rational way from SOM and to get an automatic objective map interpretation. Maps derived by SOM showed specific regions associated with a unique receptor type and zones in which two or more activity classes are nested. Then, the modeling ability of the proposed SOM/FC Hybrid System tools applied simultaneously to eight activity classes was validated after dividing the 389 compounds into a training set and a test set, including 259 and 130 molecules, respectively. The proper experimental activity class, among the eight possible ones, was predicted simultaneously and correctly for 81% of the test set compounds.

Design, docking, and evaluation of multiple libraries against multiple targets

We present a general approach to the design, docking, and virtual screening of multiple combinatorial libraries against a family of proteins. The method consists of three main stages: docking the scaffold, selecting the best substituents at each site of diversity, and comparing the resultant molecules within and between the libraries. The core "divide-and-conquer" algorithm for side-chain selection, developed from an earlier version (Sun et al., J Comp Aided Mol Design 1998;12:597-604), provides a way to explore large lists of substituents with linear rather than combinatorial time dependence. We have applied our method to three combinatorial libraries and three serine proteases: trypsin, chymotrypsin, and elastase. We show that the scaffold docking procedure, in conjunction with a novel vector-based orientation filter, reproduces crystallographic binding modes. In addition, the free-energy-based scoring procedure (Zou et al., J Am Chem Soc 1999;121:8033-8043) is able to reproduce experimental binding data for P1 mutants of macromolecular protease inhibitors. Finally, we show that our method discriminates between a peptide library and virtual libraries built on benzodiazepine and tetrahydroisoquinolinone scaffolds. Implications of the docking results for library design are explored.

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 = 18 991). Related to hydrophobicity, the NPH descriptors are intended for fast analyses of extremely large VCLs (10(6)-10(12) compounds), even before the enumeration of reactants into products occurs.

The potential of alkaloids in drug discovery

Alkaloids are an important group of diversely distributed, chemically, biologically and commercially significant natural products. This article suggests why now, with the presently available technology, and the remaining biome available and reasonably accessible, is an opportune moment to consciously focus on the discovery of further alkaloids with pharmacophoric utility.

Successful implementation of automation in medicinal chemistry

Automation in medicinal chemistry is often seen simply as a part of the combinatorial chemistry technologies used to meet the need for large, diverse screening collections for lead generation. However, the application of automation to the lead optimization phase of drug discovery offers the prospect of reduced cycle times via increased efficiency in target compound preparation. The realization of this goal requires the integration of efficient processes with equipment capable of delivering quality compounds - and, of course, the skilled medicinal chemists.

Development and screening of a polyketide virtual library for drug leads against a motilide pharmacophore

A virtual library of macrocyclic polyketide molecules was generated and screened to identify novel, conformationally constrained potential motilin receptor agonists ("motilides"). A motilide pharmacophore model was generated from the potent 6,9-enol ether erythromycin and known derivatives from the literature. The pharmacophore for each molecular conformation was a point in a distance-volume space based on presentation of the putative binding moieties. Two methods, one fragment based method and the other reaction based, were explored for constructing the polyketide virtual library. First, a virtual library was assembled from monomeric fragments using the CHORTLES language. Second, the virtual library was assembled by the in silico application of all possible polyketide synthase enzyme reactions to generate the product library. Each library was converted to low-energy 3D conformations by distance geometry and standard minimization methods. The distance-volume metric was calculated for low-energy conformations of the members of the virtual polyketide library and screened against the enol ether pharmacophore. The goal was to identify novel macrocycles that satisfy the pharmacophore. We identified three conformationally constrained, novel polyketide series that have low-energy conformations satisfying the distance-volume constraints of the motilide pharmacophore.

Chemical information management in drug discovery: optimizing the computational and combinatorial chemistry interfaces

Structure-property relationships, central to many of today's drug discovery strategies, are not straightforward to deal with when trying to predict drug efficacy, that is, the combined outcome of target affinity, pharmacodynamic behavior, pharmacokinetic properties, and metabolic fate. In this article, we discuss the handling of chemical property information in reagents-for-synthesis selection, enumeration, and virtual library construction. We describe the use of diversity assessment and/or experimental design in selection of compound-libraries-to-be-synthesized. Our overall objective was to identify good-quality drug candidates through reliable structure-activity relationship data, with the minimum number of compounds synthesized and tested. Chemical filters, property filters, scoring functions, and utilization of interactive visualization tools are discussed. The concept of chemical diversity and aspects of chemical space navigation employing a proprietary tool, Chemical Global Positioning System (ChemGPS), for mapping the drug-related chemical space are examined. Guidelines and workflow recommendations for the practicing medicinal chemist are proposed.

Combinatorial chemistry as a tool for drug discovery

The question 'will combinatorial chemistry deliver real medicines' has been posed [96]. First it is important to realise that the chemical part of the drug discovery process cannot stand alone; the integration of synthesis and biological assays is fundamental to the combinatorial approach. The results presented in Tables 3.1 to 3.8 suggest that so far smaller directed combinatorial libraries have obtained equivalent results to those obtained previously from traditional medicinal chemistry analogue programs. Unfortunately, because of the long time it takes to develop pharmaceutical drugs there are no examples yet of marketed drugs discovered by combinatorial methods. There are interesting examples where active leads have been discovered from the screening of the same library against multiple targets (e.g. libraries 13, 39, 43, 66, 71 and 76). It is now possible to handle much larger libraries of non-oligomeric structures and the chemistry required for such applications is becoming available. Whether combinatorial approaches can also be adapted to deal with all the other requirements of a successful pharmaceutical (lack of toxicity, bioavailability etc.) is open to question but there are already examples such as cassette dosing [235-237]. However we can still be optimistic about the possibility of larger libraries producing avenues of investigation for the medicinal chemist to develop into real drugs. Combinatorial chemistry is an important tool for the medicinal chemist.

Optimization of chemical libraries by neural networks

Neural networks are finding ever-more applications in the design of combinatorial libraries. These can be divided into two types: Kohonen (self-organizing) maps, and feed-forward networks. While the number of applications is currently quite limited, a rapid increase in publications in this area can be expected in the next few years from the rapid development of general combinatorial chemistry technology.

Mining and visualizing large anticancer drug discovery databases

In order to find more effective anticancer drugs, the U.S. National Cancer Institute (NCI) screens a large number of compounds in vitro against 60 human cancer cell lines from different organs of origin. About 70,000 compounds have been tested in the program since 1990, and each tested compound can be characterized by a vector (i.e., "fingerprint") of 60 anticancer activity, or -[log(GI50)], values. GI50 is the concentration required to inhibit cell growth by 50% compared with untreated controls. Although cell growth inhibitory activity for a single cell line is not very informative, activity patterns across the 60 cell lines can provide incisive information on the mechanisms of action of screened compounds and also on molecular targets and modulators of activity within the cancer cells. Various statistical and artificial intelligence methods, including principal component analysis, hierarchical cluster analysis, stepwise linear regression, multidimensional scaling, neural network modeling, and genetic function approximation, among others, can be used to analyze this large activity database. Mining the database can provide useful information: (a) for the development of anticancer drugs; (b) for a better understanding of the molecular pharmacology of cancer; and (c) for improvement of the drug discovery process.

Structure-based drug design approaches for predicting binding affinities of HIV1 protease inhibitors

Computational assessment of the binding affinity of enzyme inhibitors prior to synthesis is an important component of computer-assisted drug design (CADD) paradigms. The free energy perturbation (FEP) methodology is the most accurate means of estimating relative binding affinities between two inhibitors. However, due to its complexity and computation-intensive nature, practical applications are restricted to analysis of structurally-related inhibitors. Accordingly, there is a need for methods that enable rapid assessment of large number of structurally-unrelated molecules in a suitably accurate manner. In this review, the FEP method is compared with regression-based methods that employ multivariate models to assess the advantages of each in the estimation of relative binding affinities of inhibitors to an enzyme. Semiquantitative predictions of relative binding free energies of human immunodeficiency virus 1 (HIV1) protease inhibitors are also presented and compared with the corresponding FEP results. The results indicate that the regression-based methods and the FEP method are useful in the semi-quantitative and quantitative assessment of relative binding affinities of enzyme inhibitors, respectively, prior to synthesis.

Automated synthesis: new tools for the organic chemist

Routine automation of organic chemistry had proved an elusive goal until the arrival of combinatorial chemistry and the economic pressures of increased drug discovery throughput. Now, several approaches have been used to automate chemical synthesis, resulting in a range of new tools, both large and small, to support the process of compound production. The availability of these tools to the organic chemist heralds the change from the traditional 'hand-crafted' philosophy to a more mechanized view of compound synthesis in drug discovery groups.

Combinatorial chemistry, automation and molecular diversity: new trends in the pharmaceutical industry

Combinatorial chemistry has emerged as a set of novel strategies for the synthesis of large sets of compounds (combinatorial libraries) for biological evaluation. Within a few years combinatorial chemistry has undergone a series of changes in trends, which are closely related to two important factors in libraries: numbers and quality. While the number of compounds in a library may be easily expressed, it is a lot more difficult to indicate the degree of quality of a library. This degree of quality can be split into two aspects: purity and diversity. The changing trends in combinatorial chemistry with respect to the strategies, the technologies, the libraries themselves (numbers and purity aspects) and the molecular diversity are outlined in this paper.

Evaluation of quantitative structure-activity relationship methods for large-scale prediction of chemicals binding to the estrogen receptor

Three different QSAR methods, Comparative Molecular Field Analysis (CoMFA), classical QSAR (utilizing the CODESSA program), and Hologram QSAR (HQSAR), are compared in terms of their potential for screening large data sets of chemicals as endocrine disrupting compounds (EDCs). While CoMFA and CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) have been commercially available for some time, HQSAR is a novel QSAR technique. HQSAR attempts to correlate molecular structure with biological activity for a series of compounds using molecular holograms constructed from counts of sub-structural molecular fragments. In addition to using r2 and q2 (cross-validated r2) in assessing the statistical quality of QSAR models, another statistical parameter was defined to be the ratio of the standard error to the activity range. The statistical quality of the QSAR models constructed using CoMFA and HQSAR techniques were comparable and were generally better than those produced with CODESSA. It is notable that only 2D-connectivity, bond and elemental atom-type information were considered in building HQSAR models. Since HQSAR requires no conformational analysis or structural alignment, it is straightforward to use and lends itself readily to the rapid screening of large numbers of compounds. Among the QSAR methods considered, HQSAR appears to offer many attractive features, such as speed, reproducibility and ease of use, which portend its utility for prioritizing large numbers of potential EDCs for subsequent toxicological testing and risk assessment.

Computational methods in molecular diversity and combinatorial chemistry

Molecular diversity, combinatorial chemistry and automated synthesis are helping usher in a new age in medicinal chemistry. The tools and practices of computational chemistry and molecular modeling are rising to the challenges and opportunities presented by the current trends in drug discovery and design. Recent advances include a number of new and meaningful measures of molecular diversity and the use of genetic algorithms to help design diverse libraries.

The chemical-biological interface: developments in automated and miniaturised screening technology

The rapidly changing developments in genomics and combinatorial chemistry, generating new drug targets and large numbers of compounds, have caused a revolution in high-throughput screening technologies. Key to this revolution has been the introduction of robotics and automation, together with new biological assay technologies (e.g., homogeneous time resolved fluorescence). With ever increasing workloads, together with economic and logistical constraints, miniaturisation is rapidly becoming essential for the future of high-throughput screening and combinatorial chemistry. This is evident from the introduction of high-density microtitre plates, small volume liquid handling robots and associated detection technology.