The Pharmacophore Network: A Computational Method for Exploring Structure–Activity Relationships from a Large Chemical Data Set

Métivier, Jean-Philippe; Cuissart, Bertrand; Bureau, Ronan; Lepailleur, Alban

doi:10.1021/acs.jmedchem.7b01890

Cited by 22 publications

(43 citation statements)

References 54 publications

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“…In this study, compounds whose pKi values were less than 6.0 were also regarded as inactive according to the previous research. 22 Compounds with the molecular weight of less than 100 and more than 800 were discarded. During the calculation of NScaffold scores, we tested two types of scaffolds: Bemis-Murcko (BM) scaffolds 28 and scaffolds based on compound-core relationships (CCR).…”

Section: Compound Data Setsmentioning

confidence: 99%

Exploring Topological Pharmacophore Graphs for Scaffold Hopping

Nakano

Miyao

Funatsu

2020

J. Chem. Inf. Model.

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Primary goal of ligand-based virtual screening is to identify active compounds consisting of a core scaffold that is not found in the current active compound pool. Scaffold-hopping is the term used for this purpose. In the present study, topological representations of pharmacophore features on chemical graphs were investigated for scaffold-hopping. Pharmacophore Graphs (PhGs), which consist of pharmacophore features as nodes and their topological distances as edges, were used as a representation of important information of compounds being active. We investigated ranking methods for prioritizing PhGs for scaffold hopping. The proposed method: NScaffold, which ranks PhGs based on the number of scaffolds covered by the PhGs, outperforms other conventional methods. As a demonstrative case, using a thrombin inhibitor data set, we interpreted the highest ranked PhGs by NScaffold from the protein-ligand interaction point of view. It resulted that the NScaffold method successfully retrieved three known important interactions, showing potential for identifying scaffold hopped compounds with interpretable PhGs.

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Section: Compound Data Setsmentioning

confidence: 99%

Exploring Topological Pharmacophore Graphs for Scaffold Hopping

Nakano

Miyao

Funatsu

2020

J. Chem. Inf. Model.

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“…plus compounds displaying low to no affinity for ERα should be used as negative compounds to evaluate and refine the model. The recent work of Vogt , that proposes a protocol to predict compound profile from machine learning methods and the Métivier et al pharmacophore network analysis that helps identify relations between chemical series and understanding of the influence of a chemical feature on activity or affinity are perfect examples of what could be performed using the annotations provided in the NR-DBIND.…”

Section: Discussionmentioning

confidence: 99%

Nuclear Receptors Database Including Negative Data (NR-DBIND): A Database Dedicated to Nuclear Receptors Binding Data Including Negative Data and Pharmacological Profile

et al. 2018

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Nuclear receptors (NRs) are transcription factors that regulate gene expression in various physiological processes through their interactions with small hydrophobic molecules. They constitute an important class of targets for drugs and endocrine disruptors and are widely studied for both health and environment concerns. Since the integration of negative data can be critical for accurate modeling of ligand activity profiles, we manually collected and annotated NRs interaction data (positive and negative) through a sharp review of the corresponding literature. 15 116 positive and negative interactions data are provided for 28 NRs together with 593 PDB structures in the freely available Nuclear Receptors Database Including Negative Data (http://nr-dbind.drugdesign.fr). The NR-DBIND contains the most extensive information about interaction data on NRs, which should bring valuable information to chemists, biologists, pharmacologists and toxicologists.

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“…A set of PhGs can be visualized as a network where nodes are PhGs and edges are the parent–child relation of PhGs. 15 A child PhG is created by adding a PF to the parent PhG. This type of connection is effective for visualizing a course of PhG development.…”

Section: Introductionmentioning

confidence: 99%

Visualization of Topological Pharmacophore Space with Graph Edit Distance

Nakano

Miyao

2022

ACS Omega

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A topological pharmacophore (TP) is a chemical graph-based pharmacophore representation, where nodes are pharmacophoric features (PF) and edges are topological distances between PFs. Previously proposed sparse pharmacophore graphs (SPhGs) for TPs were shown to be effective in identifying structurally different active compounds while maintaining the interpretability of the graphs. However, one limitation of using SPhGs as queries is that many structurally similar SPhGs can be identified from a set of active compounds, requiring the classification and visualization of SPhGs, followed by an understanding of the pharmacophore hypotheses. In this study, we propose a scheme for SPhG analysis based on dimensionality reduction techniques with the graph edit distance (GED) metric. This metric enables measuring similarities among SPhGs in a quantitative manner. The visualization of SPhGs, which themselves are the graphs shared by active compounds, can help us understand the pharmacophore hypotheses as well as the data set. As a proof-of-concept study, we generated two-dimensional SPhG-maps using three dimensionality reduction techniques for six biological targets. A comparison with other pharmacophore representations was also conducted. We demonstrated knowledge extraction (interpretation of the data set) from the generated maps. Our findings include a suitable mapping algorithm as well as a pharmacophore hypothesis analysis procedure using an SPhG-map.

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The Pharmacophore Network: A Computational Method for Exploring Structure–Activity Relationships from a Large Chemical Data Set

Cited by 22 publications

References 54 publications

Exploring Topological Pharmacophore Graphs for Scaffold Hopping

Exploring Topological Pharmacophore Graphs for Scaffold Hopping

Nuclear Receptors Database Including Negative Data (NR-DBIND): A Database Dedicated to Nuclear Receptors Binding Data Including Negative Data and Pharmacological Profile

Visualization of Topological Pharmacophore Space with Graph Edit Distance

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