Hierarchical Strategy for Identifying Active Chemotype Classes in Compound Databases

Scaffold Generator: a Java library implementing molecular scaffold functionalities in the Chemistry Development Kit (CDK)

The concept of molecular scaffolds as defining core structures of organic molecules is utilised in many areas of chemistry and cheminformatics, e.g. drug design, chemical classification, or the analysis of high-throughput screening data. Here, we present Scaffold Generator, a comprehensive open library for the generation, handling, and display of molecular scaffolds, scaffold trees and networks. The new library is based on the Chemistry Development Kit (CDK) and highly customisable through multiple settings, e.g. five different structural framework definitions are available. For display of scaffold hierarchies, the open GraphStream Java library is utilised. Performance snapshots with natural products (NP) from the COCONUT (COlleCtion of Open Natural prodUcTs) database and drug molecules from DrugBank are reported. The generation of a scaffold network from more than 450,000 NP can be achieved within a single day.

Automatic Identification of Analogue Series from Large Compound Data Sets: Methods and Applications

Analogue series play a key role in drug discovery. They arise naturally in lead optimization efforts where analogues are explored based on one or a few core structures. However, it is much harder to accurately identify and extract pairs or series of analogue molecules in large compound databases with no predefined core structures. This methodological review outlines the most common and recent methodological developments to automatically identify analogue series in large libraries. Initial approaches focused on using predefined rules to extract scaffold structures, such as the popular Bemis-Murcko scaffold. Later on, the matched molecular pair concept led to efficient algorithms to identify similar compounds sharing a common core structure by exploring many putative scaffolds for each compound. Further developments of these ideas yielded, on the one hand, approaches for hierarchical scaffold decomposition and, on the other hand, algorithms for the extraction of analogue series based on single-site modifications (so-called matched molecular series) by exploring potential scaffold structures based on systematic molecule fragmentation. Eventually, further development of these approaches resulted in methods for extracting analogue series defined by a single core structure with several substitution sites that allow convenient representations, such as R-group tables. These methods enable the efficient analysis of large data sets with hundreds of thousands or even millions of compounds and have spawned many related methodological developments.

A general approach for retrosynthetic molecular core analysis

Scaffold analysis of compound data sets has reemerged as a chemically interpretable alternative to machine learning for chemical space and structure–activity relationships analysis. In this context, analog series-based scaffolds (ASBS) are synthetically relevant core structures that represent individual series of analogs. As an extension to ASBS, we herein introduce the development of a general conceptual framework that considers all putative cores of molecules in a compound data set, thus softening the often applied “single molecule–single scaffold” correspondence. A putative core is here defined as any substructure of a molecule complying with two basic rules: (a) the size of the core is a significant proportion of the whole molecule size and (b) the substructure can be reached from the original molecule through a succession of retrosynthesis rules. Thereafter, a bipartite network consisting of molecules and cores can be constructed for a database of chemical structures. Compounds linked to the same cores are considered analogs. We present case studies illustrating the potential of the general framework. The applications range from inter- and intra-core diversity analysis of compound data sets, structure–property relationships, and identification of analog series and ASBS. The molecule–core network herein presented is a general methodology with multiple applications in scaffold analysis. New statistical methods are envisioned that will be able to draw quantitative conclusions from these data. The code to use the method presented in this work is freely available as an additional file. Follow-up applications include analog searching and core structure–property relationships analyses.

Reaching for the bright StARs in chemical space

Visualization of activity data in chemical space is common in drug discovery. Navigating the space in a systematic manner is not trivial, given its size and huge coverage. To this end, methods for data visualization have been developed charting biological activity into chemical space. Herein, we review the progress in different visualization approaches to explore the chemical space aiming at reaching insightful structure-activity relationships (SARs) in the chemical space. We discuss recent methods including consensus diversity plots, ChemMaps, and constellation plots. Several of the methods we review can be extended to analyze other properties of interest in medicinal chemistry, such as structure-toxicity relationships, and can be adapted to postprocess results of virtual screening (VS) of large compound libraries.

Chemical space of naturally occurring compounds

The chemical space of naturally occurring compounds is vast and diverse. Other than biologics, naturally occurring small molecules include a large variety of compounds covering natural products from different sources such as plant, marine, and fungi, to name a few, and several food chemicals. The systematic exploration of the chemical space of naturally occurring compounds have significant implications in many areas of research including but not limited to drug discovery, nutrition, bio- and chemical diversity analysis. The exploration of the coverage and diversity of the chemical space of compound databases can be carried out in different ways. The approach will largely depend on the criteria to define the chemical space that is commonly selected based on the goals of the study. This chapter discusses major compound databases of natural products and cheminformatics strategies that have been used to characterize the chemical space of natural products. Recent exemplary studies of the chemical space of natural products from different sources and their relationships with other compounds are also discussed. We also present novel chemical descriptors and data mining approaches that are emerging to characterize the chemical space of naturally occurring compounds.

Synthesis and antitubercular activity of new N-[5-(4-chlorophenyl)-1,3,4-oxadiazol-2-yl]-(nitroheteroaryl)carboxamides

Nitro-substituted heteroaromatic carboxamides 1a-e were synthesized and tested against three Mycobacterium tuberculosis cell lines. The activities can be explained in terms of the distribution of the electronic density across the nitro-substituted heteroaromatic ring attached to the amide group. 1,3,5-Oxadiazole derivatives 1c-e are candidates for the development of novel antitubercular agents. Ongoing studies are focused on exploring the mechanism by which these compounds inhibit M. tuberculosis cell growth.

Exploring the chemical space and the bioactivity profile of lactams: a chemoinformatic study

Lactams are a class of compounds important for drug design, due to their great variety of potential therapeutic applications, spanning cancer, diabetes, and infectious diseases. So far, the biological profile and chemical diversity of lactams have not been characterized in a systematic and detailed manner. In this work, we report the chemoinformatic analysis of beta-, gamma-, delta- and epsilon-lactams present in databases of approved drugs, natural products, and bioactive compounds from the large public database ChEMBL. We identified the main biological targets in which the lactams have been evaluated according to their chemical classification. We also identified the most frequent scaffolds and those that can be prioritized in chemical synthesis, since they are scaffolds with potential biological activity but with few reported analogs. Results of the biological and chemoinformatic analysis of lactams indicate that spiro- and bridged-lactams belong to classes with the lowest number of compounds and unique scaffolds, and some showing activity against specific targets. Information obtained from this analysis allows focusing the design of new chemical structures in less explored spaces and with increased possibilities of success.

Lactams are a class of compounds important for drug design, due to their great variety of potential therapeutic applications, spanning cancer, diabetes, and infectious diseases.

A chemical space odyssey of inhibitors of histone deacetylases and bromodomains

The interest in epigenetic drug and probe discovery is growing as reflected in the large amount of structure-epigenetic activity information available.

Activity cliffs and activity cliff generators based on chemotype-related activity landscapes

Activity cliffs have large impact in drug discovery; therefore, their detection and quantification are of major importance. This work introduces the metric activity cliff enrichment factor and expands the previously reported activity cliff generator concept by adding chemotype information to representations of the activity landscape. To exemplify these concepts, three molecular databases with multiple biological activities were characterized. Compounds in each database were grouped into chemotype classes. Then, pairwise comparisons of structure similarities and activity differences were calculated for each compound and used to construct chemotype-based structure-activity similarity (SAS) maps. Different landscape distributions among four major regions of the SAS maps were observed for different subsets of molecules grouped in chemotypes. Based on this observation, the activity cliff enrichment factor was calculated to numerically detect chemotypes enriched in activity cliffs. Several chemotype classes were detected having major proportion of activity cliffs than the entire database. In addition, some chemotype classes comprising compounds with smooth structure activity relationships (SAR) were detected. Finally, the activity cliff generator concept was applied to compounds grouped in chemotypes to extract valuable SAR information.

Epigenetic relevant chemical space: a chemoinformatic characterization of inhibitors of DNA methyltransferases

The first comprehensive exploration of the epigenetic relevant chemical space is reported in this work with a special emphasis on inhibitors of DNA methyltransferases.

Molpher: a software framework for systematic chemical space exploration

BACKGROUND

Chemical space is virtual space occupied by all chemically meaningful organic compounds. It is an important concept in contemporary chemoinformatics research, and its systematic exploration is vital to the discovery of either novel drugs or new tools for chemical biology.

RESULTS

In this paper, we describe Molpher, an open-source framework for the systematic exploration of chemical space. Through a process we term 'molecular morphing', Molpher produces a path of structurally-related compounds. This path is generated by the iterative application of so-called 'morphing operators' that represent simple structural changes, such as the addition or removal of an atom or a bond. Molpher incorporates an optimized parallel exploration algorithm, compound logging and a two-dimensional visualization of the exploration process. Its feature set can be easily extended by implementing additional morphing operators, chemical fingerprints, similarity measures and visualization methods. Molpher not only offers an intuitive graphical user interface, but also can be run in batch mode. This enables users to easily incorporate molecular morphing into their existing drug discovery pipelines.

CONCLUSIONS

Molpher is an open-source software framework for the design of virtual chemical libraries focused on a particular mechanistic class of compounds. These libraries, represented by a morphing path and its surroundings, provide valuable starting data for future in silico and in vitro experiments. Molpher is highly extensible and can be easily incorporated into any existing computational drug design pipeline.

Design of chemical libraries for screening

IMPORTANCE OF THE FIELD

The ultimate goal of discovery screening is to have a fast and cost-effective strategy to meet the demands of producing high-content lead series with improved prospects for clinical success. While high-throughput screening (HTS) dominates the drug discovery landscape, other processes and technologies have emerged, including high-content screening and fragment-based design to provide alternatives that may be more suitable for certain targets. There has been a growing interest in reducing the number of compounds to be screened to prevent the escalation in the costs, time and resources associated with HTS campaigns. Library design plays a central role in these efforts.

AREAS COVERED IN THIS REVIEW

This opinion provides a survey of some recent developments in the diversity based library design process, but within a historical context. In particular, the importance of chemotyping and substructure analysis and the challenges presented by novel lead discovery technologies that require the design of libraries for screening are discussed.

WHAT THE READER WILL GAIN

Readers will gain an appreciation of some developments in the field of library design and the factors that are driving the development of new library design technologies; specifically, challenges presented for chemoinformatics with the novel screening technologies in diversity based screening and compound filtering.

TAKE HOME MESSAGE

Chemotyping and substrutural analysis are techniques that have been underutilized in the process of library design. However, they offer a direct way to evaluate libraries and have been successfully used to develop predictive methodologies. Tools are available to this end, but the full power of the approach has not been realized yet.

CASE plots for the chemotype-based activity and selectivity analysis: a CASE study of cyclooxygenase inhibitors

Structure-activity characterization of molecular databases plays a central role in drug discovery. However, the characterization of large databases containing structurally diverse molecules with several end-points represents a major challenge. For this purpose, the use of chemoinformatic methods plays an important role to elucidate structure-activity relationships. Herein, a general methodology, namely Chemotype Activity and Selectivity Enrichment plots, is presented. Chemotype Activity and Selectivity Enrichment plots provide graphical information concerning the activity and selectivity patterns of particular chemotypes contained in structurally diverse databases. As a case study, we analyzed a set of 658 compounds screened against cyclooxygenase-1 and cyclooxygenase-2. Chemotype Activity and Selectivity Enrichment plots analysis highlighted chemotypes enriched with active and selective molecules against cyclooxygenase-2; all this in a simple 2D graphical representation. Additionally, the most active and selective chemotypes detected in Chemotype Activity and Selectivity Enrichment plots were analyzed separately using the previously reported dual activity-difference maps. These findings indicate that Chemotype Activity and Selectivity Enrichment plots and dual activity-difference maps are complementary chemoinformatic tools to explore the structure-activity relationships of structurally diverse databases screened against two biological end-points.

Molecular scaffold analysis of natural products databases in the public domain

Natural products represent important sources of bioactive compounds in drug discovery efforts. In this work, we compiled five natural products databases available in the public domain and performed a comprehensive chemoinformatic analysis focused on the content and diversity of the scaffolds with an overview of the diversity based on molecular fingerprints. The natural products databases were compared with each other and with a set of molecules obtained from in-house combinatorial libraries, and with a general screening commercial library. It was found that publicly available natural products databases have different scaffold diversity. In contrast to the common concept that larger libraries have the largest scaffold diversity, the largest natural products collection analyzed in this work was not the most diverse. The general screening library showed, overall, the highest scaffold diversity. However, considering the most frequent scaffolds, the general reference library was the least diverse. In general, natural products databases in the public domain showed low molecule overlap. In addition to benzene and acyclic compounds, flavones, coumarins, and flavanones were identified as the most frequent molecular scaffolds across the different natural products collections. The results of this work have direct implications in the computational and experimental screening of natural product databases for drug discovery.

A cell-based fascin bioassay identifies compounds with potential anti-metastasis or cognition-enhancing functions

The actin-bundling protein fascin is a key mediator of tumor invasion and metastasis and its activity drives filopodia formation, cell-shape changes and cell migration. Small-molecule inhibitors of fascin block tumor metastasis in animal models. Conversely, fascin deficiency might underlie the pathogenesis of some developmental brain disorders. To identify fascin-pathway modulators we devised a cell-based assay for fascin function and used it in a bidirectional drug screen. The screen utilized cultured fascin-deficient mutant Drosophila neurons, whose neurite arbors manifest the 'filagree' phenotype. Taking a repurposing approach, we screened a library of 1040 known compounds, many of them FDA-approved drugs, for filagree modifiers. Based on scaffold distribution, molecular-fingerprint similarities, and chemical-space distribution, this library has high structural diversity, supporting its utility as a screening tool. We identified 34 fascin-pathway blockers (with potential anti-metastasis activity) and 48 fascin-pathway enhancers (with potential cognitive-enhancer activity). The structural diversity of the active compounds suggests multiple molecular targets. Comparisons of active and inactive compounds provided preliminary structure-activity relationship information. The screen also revealed diverse neurotoxic effects of other drugs, notably the 'beads-on-a-string' defect, which is induced solely by statins. Statin-induced neurotoxicity is enhanced by fascin deficiency. In summary, we provide evidence that primary neuron culture using a genetic model organism can be valuable for early-stage drug discovery and developmental neurotoxicity testing. Furthermore, we propose that, given an appropriate assay for target-pathway function, bidirectional screening for brain-development disorders and invasive cancers represents an efficient, multipurpose strategy for drug discovery.

Data mining of protein-binding profiling data identifies structural modifications that distinguish selective and promiscuous compounds

Activity profiling of compound collections across multiple targets is increasingly being used in probe and drug discovery. Herein, we discuss an approach to systematically analyzing the structure-activity relationships of a large screening profile data with emphasis on identifying structural changes that have a significant impact on the number of proteins to which a compound binds. As a case study, we analyzed a recently released public data set of more than 15 000 compounds screened across 100 sequence-unrelated proteins. The screened compounds have different origins and include natural products, synthetic molecules from academic groups, and commercial compounds. Similar synthetic structures from academic groups showed, overall, greater promiscuity differences than do natural products and commercial compounds. The method implemented in this work readily identified structural changes that differentiated highly specific from promiscuous compounds. This approach is general and can be applied to analyze any other large-scale protein-binding profile data.

Benzotriazoles and indazoles are scaffolds with biological activity against Entamoeba histolytica

Parasitic infections caused by Entamoeba histolytica are still major threats against public health, especially in developing countries. Although current therapies exist, the problems associated with parasite resistance and negative side effects make it imperative to search for new therapeutic agents. A systematic scaffold analysis reported herein of a public database containing 474 antiamoebic compounds reveals that benzimidazole is the most active scaffold reported thus far. To gain insights into the antiamoebic activity of novel compounds, the authors report herein the biological activity of 12 compounds, including benzotriazole and indazole derivatives, scaffolds not previously tested against E. histolytica. Compounds with the benzotriazole and indazole scaffolds showed low micromolar activity (IC(50) = 0.304 and 0.339 µM) and are more active than metronidazole, which is the drug of choice used for the treatment of amebiosis. The novel compounds have similar properties to approved drugs. Compounds with novel scaffolds represent promising starting points of an optimization program against E. histolytica.

Some Trends in Chem(o)informatics

This introductory chapter gives a brief overview of the history of cheminformatics, and then summarizes some recent trends in computing, cultures, open systems, chemical structure representation, docking, de novo design, fragment-based drug design, molecular similarity, quantitative structure-activity relationships (QSAR), metabolite prediction, the use of phamacophores in drug discovery, data reduction and visualization, and text mining. The aim is to set the scene for the more detailed exposition of these topics in the later chapters.

Natural products as DNA methyltransferase inhibitors: a computer-aided discovery approach

DNA methyltransferases (DNMTs) represent promising targets for the development of unique anticancer drugs. However, all DNMT inhibitors currently in clinical use are nonselective cytosine analogs with significant cytotoxic side-effects. Several natural products, covering diverse chemical classes, have indicated DNMT inhibitory activity, but these effects have yet to be systematically evaluated. In this study, we provide experimental data suggesting that two of the most prominent natural products associated with DNA methylation inhibition, (-)-epigallocathechin-3-gallate (EGCG) and curcumin, have little or no pharmacologically relevant inhibitory activity. We therefore conducted a virtual screen of a large database of natural products with a validated homology model of the catalytic domain of DNMT1. The virtual screening focused on a lead-like subset of the natural products docked with DNMT1, using three docking programs, following a multistep docking approach. Prior to docking, the lead-like subset was characterized in terms of chemical space coverage and scaffold content. Consensus hits with high predicted docking affinity for DNMT1 by all three docking programs were identified. One hit showed DNMT1 inhibitory activity in a previous study. The virtual screening hits were located within the biological-relevant chemical space of drugs, and represent potential unique DNMT inhibitors of natural origin. Validation of these virtual screening hits is warranted.

The development of a knowledge base for basic active structures: an example case of dopamine agonists

BACKGROUND

Chemical compounds affecting a bioactivity can usually be classified into several groups, each of which shares a characteristic substructure. We call these substructures "basic active structures" or BASs. The extraction of BASs is challenging when the database of compounds contains a variety of skeletons. Data mining technology, associated with the work of chemists, has enabled the systematic elaboration of BASs.

RESULTS

This paper presents a BAS knowledge base, BASiC, which currently covers 46 activities and is available on the Internet. We use the dopamine agonists D1, D2, and Dauto as examples and illustrate the process of BAS extraction. The resulting BASs were reasonably interpreted after proposing a few template structures.

CONCLUSIONS

The knowledge base is useful for drug design. Proposed BASs and their supporting structures in the knowledge base will facilitate the development of new template structures for other activities, and will be useful in the design of new lead compounds via reasonable interpretations of active structures.

Similarity searching using 2D structural fingerprints

This chapter reviews the use of molecular fingerprints for chemical similarity searching. The fingerprints encode the presence of 2D substructural fragments in a molecule, and the similarity between a pair of molecules is a function of the number of fragments that they have in common. Although this provides a very simple way of estimating the degree of structural similarity between two molecules, it has been found to provide an effective and an efficient tool for searching large chemical databases. The review describes the historical development of similarity searching since it was first described in the mid-1980s, reviews the many different coefficients, representations, and weightings that can be combined to form a similarity measure, describes quantitative measures of the effectiveness of similarity searching, and concludes by looking at current developments based on the use of data fusion and machine learning techniques.

Chemoinformatic analysis of combinatorial libraries, drugs, natural products, and molecular libraries small molecule repository

A multiple criteria approach is presented, that is used to perform a comparative analysis of four recently developed combinatorial libraries to drugs, Molecular Libraries Small Molecule Repository (MLSMR) and natural products. The compound databases were assessed in terms of physicochemical properties, scaffolds, and fingerprints. The approach enables the analysis of property space coverage, degree of overlap between collections, scaffold and structural diversity, and overall structural novelty. The degree of overlap between combinatorial libraries and drugs was assessed using the R-NN curve methodology, which measures the density of chemical space around a query molecule embedded in the chemical space of a target collection. The combinatorial libraries studied in this work exhibit scaffolds that were not observed in the drug, MLSMR, and natural products databases. The fingerprint-based comparisons indicate that these combinatorial libraries are structurally different than current drugs. The R-NN curve methodology revealed that a proportion of molecules in the combinatorial libraries is located within the property space of the drugs. However, the R-NN analysis also showed that there are a significant number of molecules in several combinatorial libraries that are located in sparse regions of the drug space.

New directions in library design and analysis

The high costs associated with high-throughput screening (HTS) coupled with the limited coverage and bias of current screening collections is such that diversity analysis continues to be an important criterion in lead generation. Whereas early approaches to diversity analysis were based on traditional descriptors such as two-dimensional fingerprints a recent emphasis has been on assessing scaffold coverage to ensure that a variety of different chemotypes are represented. Moreover, whether designing diverse or focused libraries, it is widely recognised that designs should aim to achieve a balance in a number of different properties and multiobjective optimisation provides an effective way of achieving such designs.