Small-World Phenomena in Chemical Library Networks: Application to Fragment-Based Drug Discovery

Tanaka, Naoki; Ohno, Kazuki; Niimi, Tatsuya; Moritomo, Ayako; Mori, Kenichi; Orita, Masaya

doi:10.1021/ci900123v

Cited by 24 publications

(21 citation statements)

References 27 publications

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“…Compared to other chemical space representations, networks have the additional advantage that they can be characterized and compared in detail using a variety of statistical approaches from general network science [10,11]. However, only very few network-like representations of chemical space have been reported thus far [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures

Zhang

Vogt

Maggiora

et al. 2015

J Comput Aided Mol Des

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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.

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Section: Introductionmentioning

confidence: 99%

Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures

Zhang

Vogt

Maggiora

et al. 2015

J Comput Aided Mol Des

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show abstract

“…From the large number of numerical descriptions of similarity listed in Table 5, we will first consider those networks, which are based on simple chemical similarity of the compounds involved using e.g. the Tanimoto-coefficient for the definition of edges (Rogers & Tanimoto, 1960; Tanaka et al, 2009; Bickerton et al, 2012). We will call these networks chemical similarity networks.…”

Section: The Use Of Molecular Network In Drug Designmentioning

confidence: 99%

“…If hubs become non-hits, many fragment-combinations can be excluded as candidates, under the assumption that molecules similar to non-hits are also non-hits. This strategy was shown to explore the chemistry space in much less trials than random selection or the selection of cluster centers (Tanaka et al, 2009). Well connected fragments can also be used in library design and in fragment-based database searches.…”

Section: The Use Of Molecular Network In Drug Designmentioning

confidence: 99%

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Csermely

Korcsmáros

Kiss

et al. 2013

Pharmacology & Therapeutics

808

661

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Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only gives a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The “central hit strategy” selectively targets central node/edges of the flexible networks of infectious agents or cancer cells to kill them. The “network influence strategy” works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

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“…Such chemical space networks (CSNs) have recently been proposed as an attractive alternative to coordinate-based representations [3], although very few attempts have thus far been made to generate such chemical space views [6][7][8][9][10]. Herein, we report an attempt to systematically generate CSNs for different compound data sets and characterize and compare them with the aid of statistical methods from network science.…”

Section: Introductionmentioning

confidence: 99%

Design and characterization of chemical space networks for different compound data sets

Zwierzyna

Vogt

Maggiora

et al. 2014

J Comput Aided Mol Des

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Chemical Space Networks (CSNs) are generated for different compound data sets on the basis of pairwise similarity relationships. Such networks are thought to complement and further extend traditional coordinate-based views of chemical space. Our proof-of-concept study focuses on CSNs based upon fingerprint similarity relationships calculated using the conventional Tanimoto similarity metric. The resulting CSNs are characterized with statistical measures from network science and compared in different ways. We show that the homophily principle, which is widely considered in the context of social networks, is a major determinant of the topology of CSNs of bioactive compounds, designed as threshold networks, typically giving rise to community structures. Many properties of CSNs are influenced by numerical features of the conventional Tanimoto similarity metric and largely dominated by the edge density of the networks, which depends on chosen similarity threshold values. However, properties of different CSNs with constant edge density can be directly compared, revealing systematic differences between CSNs generated from randomly collected or bioactive compounds.

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Small-World Phenomena in Chemical Library Networks: Application to Fragment-Based Drug Discovery

Cited by 24 publications

References 27 publications

Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures

Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Design and characterization of chemical space networks for different compound data sets

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