The Experts below are selected from a list of 125745 Experts worldwide ranked by ideXlab platform
Jean-louis Reymond - One of the best experts on this subject based on the ideXlab platform.
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Populating Chemical Space with Peptides Using a Genetic Algorithm.
Journal of chemical information and modeling, 2020Co-Authors: Alice Capecchi, Alain Zhang, Jean-louis ReymondAbstract:In drug discovery, one uses Chemical Space as a concept to organize molecules according to their structures and properties. One often would like to generate new possible molecules at a specific location in the Chemical Space marked by a molecule of interest. Herein, we report the peptide design genetic algorithm (PDGA, code available at https://github.com/reymond-group/PeptideDesignGA ), a computational tool capable of producing peptide sequences of various topologies (linear, cyclic/polycyclic, or dendritic) in proximity of any molecule of interest in a Chemical Space defined by macromolecule extended atom-pair fingerprint (MXFP), an atom-pair fingerprint describing molecular shape and pharmacophores. We show that the PDGA generates high-similarity analogues of bioactive peptides with diverse peptide chain topologies and of nonpeptide target molecules. We illustrate the Chemical Space accessible by the PDGA with an interactive 3D map of the MXFP property Space available at http://faerun.gdb.tools/ . The PDGA should be generally useful to generate peptides at any location in the Chemical Space.
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Exploring Chemical Space with Machine Learning
Chimia, 2019Co-Authors: Josep Arús-pous, Mahendra Awale, Daniel Probst, Jean-louis ReymondAbstract:Chemical Space is a concept to organize molecular diversity by postulating that different molecules occupy different regions of a mathematical Space where the position of each molecule is defined by its properties. Our aim is to develop methods to explicitly explore Chemical Space in the area of drug discovery. Here we review our implementations of machine learning in this project, including our use of deep neural networks to enumerate the GDB13 database from a small sample set, to generate analogs of drugs and natural products after training with fragment-size molecules, and to predict the polypharmacology of molecules after training with known bioactive compounds from ChEMBL. We also discuss visualization methods for big data as means to keep track and learn from machine learning results. Computational tools discussed in this review are freely available at http://gdb.unibe.ch and https://github.com/reymond-group.
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Populating Chemical Space with Peptides using a Genetic Algorithm
2019Co-Authors: Alice Capecchi, Alain Zhang, Jean-louis ReymondAbstract:In drug discovery one uses Chemical Space as a concept to organize molecules according to their structures and properties. One often would like to generate new possible molecules at a specific location in Chemical Space marked by a molecule of interest. Herein we report the peptide design genetic algorithm (PDGA, code available at https://github.com/reymondgroup/PeptideDesignGA), a computational tool capable of producing peptide sequences of various chain topologies (linear, cyclic/polycyclic or dendritic) in proximity of any molecule of interest in a Chemical Space defined by MXFP, an atom-pair fingerprint describing molecular shape and pharmacophores. We show that PDGA generates high similarity analogs of bioactive peptides, including in selected cases known active analogs, as well as of non-peptide targets. We illustrate the Chemical Space accessible by PDGA with an interactive 3D-map of the MXFP property Space available at http://faerun.gdb.tools/. PDGA should be generally useful to generate peptides at any location in Chemical Space.
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exploring the gdb 13 Chemical Space using deep generative models
Journal of Cheminformatics, 2019Co-Authors: Jean-louis Reymond, Josep Aruspous, Thomas Blaschke, Silas Ulander, Hongming Chen, Ola EngkvistAbstract:Recent applications of recurrent neural networks (RNN) enable training models that sample the Chemical Space. In this study we train RNN with molecular string representations (SMILES) with a subset of the enumerated database GDB-13 (975 million molecules). We show that a model trained with 1 million structures (0.1% of the database) reproduces 68.9% of the entire database after training, when sampling 2 billion molecules. We also developed a method to assess the quality of the training process using negative log-likelihood plots. Furthermore, we use a mathematical model based on the “coupon collector problem” that compares the trained model to an upper bound and thus we are able to quantify how much it has learned. We also suggest that this method can be used as a tool to benchmark the learning capabilities of any molecular generative model architecture. Additionally, an analysis of the generated Chemical Space was performed, which shows that, mostly due to the syntax of SMILES, complex molecules with many rings and heteroatoms are more difficult to sample.
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Exploring DrugBank in Virtual Reality Chemical Space
Journal of chemical information and modeling, 2018Co-Authors: Daniel Probst, Jean-louis ReymondAbstract:The recent general availability of low-cost virtual reality headsets and accompanying three-dimensional (3D) engine support presents an opportunity to bring the concept of Chemical Space into virtual environments. While virtual reality applications represent a category of widespread tools in other fields, their use in the visualization and exploration of abstract data such as Chemical Spaces has been experimental. In our previous work, we established the concept of interactive two-dimensional (2D) maps of Chemical Spaces followed by interactive web-based 3D visualizations, culminating in the interactive web-based 3D visualization of extremely large Chemical Spaces. Virtual reality Chemical Spaces are a natural extension of these concepts. As 2D and 3D embeddings and projections of high-dimensional Chemical fingerprint Spaces have been shown to be valuable tools in Chemical Space visualization and exploration, existing pipelines of data mining and preparation can be extended to be used in virtual reality applications. Here we present an application based on the Unity engine and the Virtual Reality Toolkit, allowing for the interactive exploration of Chemical Space populated by DrugBank compounds in virtual reality. The source code of the application as well as the most recent build are available on GitHub ( https://github.com/reymond-group/virtual-reality-Chemical-Space ).
José L. Medina-franco - One of the best experts on this subject based on the ideXlab platform.
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Reaching for the bright StARs in Chemical Space.
Drug discovery today, 2019Co-Authors: José L. Medina-franco, J. Jesús Naveja, Edgar López-lópezAbstract: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.
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Finding Constellations in Chemical Space Through Core Analysis.
Frontiers in chemistry, 2019Co-Authors: J. Jesús Naveja, José L. Medina-francoAbstract:Herein we introduce the constellation plots as a general approach that merges different and complementary molecular representations to enhance the information contained in a visual representation and analysis of Chemical Space. The method is based on a combination of a sub-structure based representation and classification of compounds with a "classical" coordinate-based representation of Chemical Space. A distinctive outcome of the method is that organizing the compounds in analog series leads to the formation of groups of molecules, aka "constellations" in Chemical Space. The novel approach is general and can be used to rapidly identify, for instance, insightful and "bright" Structure-Activity Relationships (StARs) in Chemical Space that are easy to interpret. This kind of analysis is expected to be especially useful for lead identification in large datasets of unannotated molecules, such as those obtained through high-throughput screening. We demonstrate the application of the method using two datasets of focused inhibitors designed against DNMTs and AKT1.
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Chemical Space of naturally occurring compounds
Physical Sciences Reviews, 2018Co-Authors: Fernanda I. Saldívar-gonzález, B. Angélica Pilón-jiménez, José L. Medina-francoAbstract:AbstractThe 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.
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Exploring the Chemical Space of peptides for drug discovery: a focus on linear and cyclic penta-peptides
Molecular Diversity, 2018Co-Authors: Bárbara I. Díaz-eufracio, Richard A Houghten, Oscar Palomino-hernández, José L. Medina-francoAbstract:Peptide and peptide-like structures are regaining attention in drug discovery. Previous studies suggest that bioactive peptides have diverse structures and may have physicoChemical properties attractive to become hit and lead compounds. However, chemoinformatic studies that characterize such diversity are limited. Herein, we report the physicoChemical property profile and Chemical Space of four synthetic linear and cyclic combinatorial peptide libraries. As a case study, the analysis was focused on penta-peptides. The Chemical Space of the peptide and N-methylated peptides libraries was compared to compound data sets of pharmaceutical relevance. Results indicated that there is a major overlap in the Chemical Space of N-methylated cyclic peptides with inhibitors of protein–protein interactions and macrocyclic natural products available for screening. Also, there is an overlap between the Chemical Space of the synthetic peptides with peptides approved for clinical use (or in clinical trials), and to other approved drugs that are outside the traditional Chemical Space. Results further support that synthetic penta-peptides are suitable compounds to be used in drug discovery projects.
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ChemMaps: Towards an approach for visualizing the Chemical Space based on adaptive satellite compounds
F1000Research, 2017Co-Authors: J. Jesús Naveja, José L. Medina-francoAbstract:We present a novel approach called ChemMaps for visualizing Chemical Space based on the similarity matrix of compound datasets generated with molecular fingerprints’ similarity. The method uses a ‘satellites’ approach, where satellites are, in principle, molecules whose similarity to the rest of the molecules in the database provides sufficient information for generating a visualization of the Chemical Space. Such an approach could help make Chemical Space visualizations more efficient. We hereby describe a proof-of-principle application of the method to various databases that have different diversity measures. Unsurprisingly, we found the method works better with databases that have low 2D diversity. 3D diversity played a secondary role, although it becomes increasingly relevant as 2D diversity increases. For less diverse datasets, taking as few as 25% satellites seems to be sufficient for a fair depiction of the Chemical Space. We propose to iteratively increase the satellites number by a factor of 5% relative to the whole database, and stop when the new and the prior Chemical Space correlate highly. This Research Note warrants the full application of this method for several datasets.
Herbert Waldmann - One of the best experts on this subject based on the ideXlab platform.
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Charting, navigating, and populating natural product Chemical Space for drug discovery
Journal of Medicinal Chemistry, 2012Co-Authors: Hugo Lachance, Stefan Wetzel, Kamal Kumar, Herbert WaldmannAbstract:Natural products are a heterogeneous group of compounds with diverse, yet particular molecular properties compared to synthetic compounds and drugs. All relevant analyses show that natural products indeed occupy parts of Chemical Space not explored by available screening collections while at the same time largely adhering to the rule-of-five. This renders them a valuable, unique, and necessary component of screening libraries used in drug discovery. With ChemGPS-NP on the Web and Scaffold Hunter two tools are available to the scientific community to guide exploration of biologically relevant NP Chemical Space in a focused and targeted fashion with a view to guide novel synthesis approaches. Several of the examples given illustrate the possibility of bridging the gap between computational methods and compound library synthesis and the possibility of integrating cheminformatics and Chemical Space analyses with synthetic chemistry and biochemistry to successfully explore Chemical Space for the identification of novel small molecule modulators of protein function.The examples also illustrate the synergistic potential of the Chemical Space concept and modern Chemical synthesis for biomedical research and drug discovery. Chemical Space analysis can map under explored biologically relevant parts of Chemical Space and identify the structure types occupying these parts. Modern synthetic methodology can then be applied to efficiently fill this “virtual Space” with real compounds.From a cheminformatics perspective, there is a clear demand for open-source and easy to use tools that can be readily applied by educated nonspecialist chemists and biologists in their daily research. This will include further development of Scaffold Hunter, ChemGPS-NP, and related approaches on the Web. Such a “cheminformatics toolbox” would enable chemists and biologists to mine their own data in an intuitive and highly interactive process and without the need for specialized computer science and cheminformatics expertise. We anticipate that it may be a viable, if not necessary, step for research initiatives based on large high-throughput screening campaigns,in particular in the pharmaceutical industry, to make the most out of the recent advances in computational tools in order to leverage and take full advantage of the large data sets generated and available in house. There are “holes” in these data sets that can and should be identified and explored by chemistry and biology.
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bioactivity guided navigation of Chemical Space
Accounts of Chemical Research, 2010Co-Authors: Robin S Bon, Herbert WaldmannAbstract:A central aim of biological research is to elucidate the many roles of proteins in complex, dynamic living systems; the selective perturbation of protein function is an important tool in achieving this goal. Because Chemical perturbations offer opportunities often not accessible with genetic methods, the development of small-molecule modulators of protein function is at the heart of Chemical biology research. In this endeavor, the identification of biologically relevant starting points within the vast Chemical Space available for the design of compound collections is a particularly relevant, yet difficult, task. In this Account, we present our research aimed at linking Chemical and biological Space to define suitable starting points that guide the synthesis of compound collections with biological relevance. Both protein folds and natural product (NP) scaffolds are highly conserved in nature. Whereas different amino acid sequences can make up ligand-binding sites in proteins with highly similar fold types,...
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Interactive exploration of Chemical Space with Scaffold Hunter.
Nature chemical biology, 2009Co-Authors: Stefan Wetzel, Tudor I. Oprea, Steffen Renner, Karsten Klein, Daniel Rauh, Petra Mutzel, Herbert WaldmannAbstract:We describe Scaffold Hunter, a highly interactive computer-based tool for navigation in Chemical Space that fosters intuitive recognition of complex structural relationships associated with bioactivity. The program reads compound structures and bioactivity data, generates compound scaffolds, correlates them in a hierarchical tree-like arrangement, and annotates them with bioactivity. Brachiation along tree branches from structurally complex to simple scaffolds allows identification of new ligand types. We provide proof of concept for pyruvate kinase.
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Cheminformatic Analysis of Natural Products and their Chemical Space
CHIMIA International Journal for Chemistry, 2007Co-Authors: Stefan Wetzel, Ansgar Schuffenhauer, Silvio Roggo, Peter Ertl, Herbert WaldmannAbstract:Cheminformatic methods allow the detailed characterization of particular and characteristic properties of natural products (NPs) and comparison with related characteristics of drugs and other compounds. An overview of the most important properties of natural products and analogues and their difference with respect to drugs and synthetic compounds is presented. Moreover, different approaches to charting the Chemical Space populated by natural products are reviewed and their underlying principles are delineated. Some insights about NP Chemical Space are described together with possible applications of methods charting Chemical Space. Strengths and weaknesses of the different approaches will be discussed with respect to possible applications in compound collection design.
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charting biologically relevant Chemical Space a structural classification of natural products sconp
Proceedings of the National Academy of Sciences of the United States of America, 2005Co-Authors: Marcus A Koch, Stefan Wetzel, Ansgar Schuffenhauer, Peter Ertl, Michael Scheck, Marco Casaulta, Alex Odermatt, Herbert WaldmannAbstract:The identification of small molecules that fall within the biologically relevant subfraction of vast Chemical Space is of utmost importance to Chemical biology and medicinal chemistry research. The prerequirement of biological relevance to be met by such molecules is fulfilled by natural product-derived compound collections. We report a structural classification of natural products (SCONP) as organizing principle for charting the known Chemical Space explored by nature. SCONP arranges the scaffolds of the natural products in a tree-like fashion and provides a viable analysis- and hypothesis-generating tool for the design of natural product-derived compound collections. The validity of the approach is demonstrated in the development of a previously undescribed class of selective and potent inhibitors of 11β-hydroxysteroid dehydrogenase type 1 with activity in cells guided by SCONP and protein structure similarity clustering. 11β-hydroxysteroid dehydrogenase type 1 is a target in the development of new therapies for the treatment of diabetes, the metabolic syndrome, and obesity.
Jürgen Bajorath - One of the best experts on this subject based on the ideXlab platform.
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Design of Chemical Space networks incorporating compound distance relationships
F1000Research, 2016Co-Authors: Antonio De La Vega De León, Jürgen BajorathAbstract:Networks, in which nodes represent compounds and edges pairwise similarity relationships, are used as coordinate-free representations of Chemical Space. So-called Chemical Space networks (CSNs) provide intuitive access to structural relationships within compound data sets and can be annotated with activity information. However, in such similarity-based networks, distances between compounds are typically determined for layout purposes and clarity and have no Chemical meaning. By contrast, inter-compound distances as a measure of dissimilarity can be directly obtained from coordinate-based representations of Chemical Space. Herein, we introduce a CSN variant that incorporates compound distance relationships and thus further increases the information content of compound networks. The design was facilitated by adapting the Kamada-Kawai algorithm. Kamada-Kawai networks are the first CSNs that are based on numerical similarity measures, but do not depend on chosen similarity threshold values.
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Chemical Space visualization: transforming multidimensional Chemical Spaces into similarity-based molecular networks
Future medicinal chemistry, 2016Co-Authors: Antonio De La Vega De León, Jürgen BajorathAbstract:The concept of Chemical Space is of fundamental relevance for medicinal chemistry and Chemical informatics. Multidimensional Chemical Space representations are coordinate-based. Chemical Space networks (CSNs) have been introduced as a coordinate-free representation. A computational approach is presented for the transformation of multidimensional Chemical Space into CSNs. The design of transformation CSNs (TRANS-CSNs) is based upon a similarity function that directly reflects distance relationships in original multidimensional Space. TRANS-CSNs provide an immediate visualization of coordinate-based Chemical Space and do not require the use of dimensionality reduction techniques. At low network density, TRANS-CSNs are readily interpretable and make it possible to evaluate structure-activity relationship information originating from multidimensional Chemical Space.
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Lessons learned from the design of Chemical Space networks and opportunities for new applications
Journal of Computer-Aided Molecular Design, 2016Co-Authors: Martin Vogt, Gerald M. Maggiora, Dagmar Stumpfe, Jürgen BajorathAbstract:The concept of Chemical Space is of fundamental relevance in Chemical informatics and computer-aided drug discovery. In a series of articles published in the Journal of Computer - Aided Molecular Design , principles of Chemical Space design were evaluated, molecular networks proposed as an alternative to conventional coordinate-based Chemical reference Spaces, and different types of Chemical Space networks (CSNs) constructed and analyzed. Central to the generation of CSNs was the way in which molecular similarity relationships were assessed and a primary focal point was the network-based representation of biologically relevant Chemical Space. The design and comparison of CSNs based upon alternative similarity measures can be viewed as an evolutionary path with interesting lessons learned along the way. CSN design has matured to the point that such Chemical Space representations can be used in practice. In this contribution, highlights from the sequence of CSN design efforts are discussed in context, providing a perspective for future practical applications.
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Design of Chemical Space networks on the basis of Tversky similarity
Journal of Computer-Aided Molecular Design, 2016Co-Authors: Martin Vogt, Gerald M. Maggiora, Jürgen BajorathAbstract:Chemical Space networks (CSNs) have been introduced as a coordinate-free representation of Chemical Space. In CSNs, nodes represent compounds and edges pairwise similarity relationships. These network representations are mostly used to navigate sections of biologically relevant Chemical Space. Different types of CSNs have been designed on the basis of alternative similarity measures including continuous numerical similarity values or substructure-based similarity criteria. CSNs can be characterized and compared on the basis of statistical concepts from network science. Herein, a new CSN design is introduced that is based upon asymmetric similarity assessment using the Tversky coefficient and termed TV-CSN. Compared to other CSNs, TV-CSNs have unique features. While CSNs typically contain separate compound communities and exhibit small world character, many TV-CSNs are also scale-free in nature and contain hubs, i.e., extensively connected central compounds. Compared to other CSNs, these hubs are a characteristic of TV-CSN topology. Hub-containing compound communities are of particular interest for the exploration of structure–activity relationships.
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Design of Chemical Space networks using a Tanimoto similarity variant based upon maximum common substructures.
Journal of computer-aided molecular design, 2015Co-Authors: Bijun Zhang, Gerald M. Maggiora, Martin Vogt, Jürgen BajorathAbstract:Chemical Space networks (CSNs) have recently been introduced as an alternative to other coordinate-free and coordinate-based Chemical Space representations. In CSNs, nodes represent compounds and edges pairwise similarity relationships. In addition, nodes are annotated with compound property information such as biological activity. CSNs have been applied to view biologically relevant Chemical Space in comparison to random Chemical Space samples and found to display well-resolved topologies at low edge density levels. The way in which molecular similarity relationships are assessed is an important determinant of CSN topology. Previous CSN versions were based on numerical similarity functions or the assessment of substructure-based similarity. Herein, we report a new CSN design that is based upon combined numerical and substructure similarity evaluation. This has been facilitated by calculating numerical similarity values on the basis of maximum common substructures (MCSs) of compounds, leading to the introduction of MCS-based CSNs (MCS-CSNs). This CSN design combines advantages of continuous numerical similarity functions with a robust and Chemically intuitive substructure-based assessment. Compared to earlier version of CSNs, MCS-CSNs are characterized by a further improved organization of local compound communities as exemplified by the delineation of drug-like subSpaces in regions of biologically relevant Chemical Space.
Stefan Wetzel - One of the best experts on this subject based on the ideXlab platform.
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Charting, navigating, and populating natural product Chemical Space for drug discovery
Journal of Medicinal Chemistry, 2012Co-Authors: Hugo Lachance, Stefan Wetzel, Kamal Kumar, Herbert WaldmannAbstract:Natural products are a heterogeneous group of compounds with diverse, yet particular molecular properties compared to synthetic compounds and drugs. All relevant analyses show that natural products indeed occupy parts of Chemical Space not explored by available screening collections while at the same time largely adhering to the rule-of-five. This renders them a valuable, unique, and necessary component of screening libraries used in drug discovery. With ChemGPS-NP on the Web and Scaffold Hunter two tools are available to the scientific community to guide exploration of biologically relevant NP Chemical Space in a focused and targeted fashion with a view to guide novel synthesis approaches. Several of the examples given illustrate the possibility of bridging the gap between computational methods and compound library synthesis and the possibility of integrating cheminformatics and Chemical Space analyses with synthetic chemistry and biochemistry to successfully explore Chemical Space for the identification of novel small molecule modulators of protein function.The examples also illustrate the synergistic potential of the Chemical Space concept and modern Chemical synthesis for biomedical research and drug discovery. Chemical Space analysis can map under explored biologically relevant parts of Chemical Space and identify the structure types occupying these parts. Modern synthetic methodology can then be applied to efficiently fill this “virtual Space” with real compounds.From a cheminformatics perspective, there is a clear demand for open-source and easy to use tools that can be readily applied by educated nonspecialist chemists and biologists in their daily research. This will include further development of Scaffold Hunter, ChemGPS-NP, and related approaches on the Web. Such a “cheminformatics toolbox” would enable chemists and biologists to mine their own data in an intuitive and highly interactive process and without the need for specialized computer science and cheminformatics expertise. We anticipate that it may be a viable, if not necessary, step for research initiatives based on large high-throughput screening campaigns,in particular in the pharmaceutical industry, to make the most out of the recent advances in computational tools in order to leverage and take full advantage of the large data sets generated and available in house. There are “holes” in these data sets that can and should be identified and explored by chemistry and biology.
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Bioactivity-guided mapping and navigation of Chemical Space
Nature chemical biology, 2009Co-Authors: Steffen Renner, Stefan Wetzel, Tudor I. Oprea, Ansgar Schuffenhauer, Peter Ertl, Willem A. L. Van Otterlo, Marta Dominguez Seoane, Sabine Möcklinghoff, Bettina Hofmann, Dieter SteinhilberAbstract:The structure- and chemistry-based hierarchical organization of library scaffolds in tree-like arrangements provides a valid, intuitive means to map and navigate Chemical Space. We demonstrate that scaffold trees built using bioactivity as the key selection criterion for structural simplification during tree construction allow efficient and intuitive mapping, visualization and navigation of the Chemical Space defined by a given library, which in turn allows correlation of this Chemical Space with the investigated bioactivity and further compound design. Brachiation along the branches of such trees from structurally complex to simple scaffolds with retained yet varying bioactivity is feasible at high frequency for the five major pharmaceutically relevant target classes and allows for the identification of new inhibitor types for a given target. We provide proof of principle by identifying new active scaffolds for 5-lipoxygenase and the estrogen receptor ERalpha.
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Interactive exploration of Chemical Space with Scaffold Hunter.
Nature chemical biology, 2009Co-Authors: Stefan Wetzel, Tudor I. Oprea, Steffen Renner, Karsten Klein, Daniel Rauh, Petra Mutzel, Herbert WaldmannAbstract:We describe Scaffold Hunter, a highly interactive computer-based tool for navigation in Chemical Space that fosters intuitive recognition of complex structural relationships associated with bioactivity. The program reads compound structures and bioactivity data, generates compound scaffolds, correlates them in a hierarchical tree-like arrangement, and annotates them with bioactivity. Brachiation along tree branches from structurally complex to simple scaffolds allows identification of new ligand types. We provide proof of concept for pyruvate kinase.
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Cheminformatic Analysis of Natural Products and their Chemical Space
CHIMIA International Journal for Chemistry, 2007Co-Authors: Stefan Wetzel, Ansgar Schuffenhauer, Silvio Roggo, Peter Ertl, Herbert WaldmannAbstract:Cheminformatic methods allow the detailed characterization of particular and characteristic properties of natural products (NPs) and comparison with related characteristics of drugs and other compounds. An overview of the most important properties of natural products and analogues and their difference with respect to drugs and synthetic compounds is presented. Moreover, different approaches to charting the Chemical Space populated by natural products are reviewed and their underlying principles are delineated. Some insights about NP Chemical Space are described together with possible applications of methods charting Chemical Space. Strengths and weaknesses of the different approaches will be discussed with respect to possible applications in compound collection design.
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charting biologically relevant Chemical Space a structural classification of natural products sconp
Proceedings of the National Academy of Sciences of the United States of America, 2005Co-Authors: Marcus A Koch, Stefan Wetzel, Ansgar Schuffenhauer, Peter Ertl, Michael Scheck, Marco Casaulta, Alex Odermatt, Herbert WaldmannAbstract:The identification of small molecules that fall within the biologically relevant subfraction of vast Chemical Space is of utmost importance to Chemical biology and medicinal chemistry research. The prerequirement of biological relevance to be met by such molecules is fulfilled by natural product-derived compound collections. We report a structural classification of natural products (SCONP) as organizing principle for charting the known Chemical Space explored by nature. SCONP arranges the scaffolds of the natural products in a tree-like fashion and provides a viable analysis- and hypothesis-generating tool for the design of natural product-derived compound collections. The validity of the approach is demonstrated in the development of a previously undescribed class of selective and potent inhibitors of 11β-hydroxysteroid dehydrogenase type 1 with activity in cells guided by SCONP and protein structure similarity clustering. 11β-hydroxysteroid dehydrogenase type 1 is a target in the development of new therapies for the treatment of diabetes, the metabolic syndrome, and obesity.
