inSARa: Intuitive and Interactive SAR Interpretation by Reduced Graphs and Hierarchical MCS-Based Network Navigation

Wollenhaupt, Sabrina; Baumann, Knut

doi:10.1021/ci4007547

Cited by 12 publications

(17 citation statements)

References 72 publications

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“…InSARa is a recently introduced MCS method (Wollenhaupt and Baumann, 2014) that uses reduced graphs to create tree-based output like ChemTreeMap, SPT and Scaffold Hunter. Its final representative substructures are sensitive to many parameter choices (Wollenhaupt and Baumann, 2014), which is where ChemTreeMap and inSARa differ. Our generalized T c -based distances have no tunable parameters, other than the choice of fingerprints.…”

Section: Comparing Chemtreemap To Other Sar Methodsmentioning

confidence: 99%

“…A potential concern about fingerprints is that their pair-wise comparisons may not be optimal for a global description of the data (Wollenhaupt and Baumann, 2014). Fortunately, ChemTreeMap's display uses a global, hierarchical organization to convey information at the local level and in the overall patterns across the data.…”

Section: Representation Of the Moleculesmentioning

confidence: 99%

“…Clotting factor Xa (FXa), cyclindependant kinase 2 (CDK2), p38a MAP kinase (p38a) and cytochrome P450 3A4 (CYP3A4) were chosen because their SAR are well characterized (Fontaine et al, 2005;Sutherland et al, 2004), and they have been used in previous studies for visualizing chemical data (Wawer and Bajorath, 2010;Wollenhaupt and Baumann, 2014). The data for FXa, CDK2 and p38a are from BindingDB.…”

Section: Datasetsmentioning

confidence: 99%

“…Graphical tools have been developed for cheminformatics that group structurally similar molecules together and display information about molecular structure and bioactivity (Sander et al, 2015;Schuffenhauer et al, 2007;Wawer and Bajorath, 2010;Wetzel et al, 2009;Wollenhaupt and Baumann, 2014). The previous methods usually require users to choose selection rules (Wawer and Bajorath, 2010) and tune parameters (Wollenhaupt and Baumann, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…The previous methods usually require users to choose selection rules (Wawer and Bajorath, 2010) and tune parameters (Wollenhaupt and Baumann, 2014). This can limit the utility of these tools for large, diverse sets of data that are the hallmark of bioinformatics.…”

Section: Introductionmentioning

confidence: 99%

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ChemTreeMap: an interactive map of biochemical similarity in molecular datasets

Lü¹,

Carlson

2016

Bioinformatics

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Motivation: What if you could explain complex chemistry in a simple tree and share that data online with your collaborators? Computational biology often incorporates diverse chemical data to probe a biological question, but the existing tools for chemical data are ill-suited for the very large datasets inherent to bioinformatics. Furthermore, existing visualization methods often require an expert chemist to interpret the patterns. Biologists need an interactive tool for visualizing chemical information in an intuitive, accessible way that facilitates its integration into today's team-based biological research. Results: ChemTreeMap is an interactive, bioinformatics tool designed to explore chemical space and mine the relationships between chemical structure, molecular properties, and biological activity. ChemTreeMap synergistically combines extended connectivity fingerprints and a neighbor-joining algorithm to produce a hierarchical tree with branch lengths proportional to molecular similarity. Compound properties are shown by leaf color, size and outline to yield a user-defined visualization of the tree. Two representative analyses are included to demonstrate ChemTreeMap's capabilities and utility: assessing dataset overlap and mining structure-activity relationships. Availability and Implementation: The examples from this paper may be accessed at http://ajing. github.io/ChemTreeMap/. Code for the server and client are available in the Supplementary Information, at the aforementioned github site, and on Docker Hub (https://hub.docker.com) with the nametag ajing/chemtreemap.

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Section: Comparing Chemtreemap To Other Sar Methodsmentioning

confidence: 99%

Section: Representation Of the Moleculesmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ChemTreeMap: an interactive map of biochemical similarity in molecular datasets

Lü¹,

Carlson

2016

Bioinformatics

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Navigating pharmacophore space to identify activity discontinuities: A case study with BCR‐ABL

Lejmi,

Geslin,

Bureau

et al. 2024

Molecular Informatics

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The exploration of chemical space is a fundamental aspect of chemoinformatics, particularly when one explores a large compound data set to relate chemical structures with molecular properties. In this study, we extend our previous work on chemical space visualization at the pharmacophoric level. Instead of using conventional binary classification of affinity (active vs inactive), we introduce a refined approach that categorizes compounds into four distinct classes based on their activity levels: super active, very active, active, and inactive. This classification enriches the color scheme applied to pharmacophore space, where the color representation of a pharmacophore hypothesis is driven by the associated compounds. Using the BCR‐ABL tyrosine kinase as a case study, we identified intriguing regions corresponding to pharmacophore activity discontinuities, providing valuable insights for structure‐activity relationships analysis.

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Hydrogen Bonding in 1‐Propanol‐Ethanol Binary Mixture: Experimental and Modeling Approaches

Swathi

Abdulkareem

Kartha³

et al. 2022

ChemistrySelect

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Hydrogen bonds between the constituent molecules determine the physical properties of binary liquids. In this work, we explore the nature of hydrogen bonding in ethanol‐propanol system through experimental and computational techniques. The refractive index, dielectric spectroscopy and infrared spectroscopy along with molecular dynamic simulations of 1‐propanol‐ethanol binary system over the entire concentration range are reported here. The excess static permittivity and excess relaxation time shows the evidence of hydrogen bonded multimer structures. The deconvoluted OH peaks from IR spectra indicate the presence of trimers, tetramers and pentamers. The linear decrease in refractive index indicates the absence of caged structures. Molecular Dynamics simulations confirm the presence of the multimers and the absence of caged structures. Radial distribution function shows the binary mixtures exhibit no structural change throughout the concentration range. We also examine the H‐bonding networks present in these systems via graph theoretic analysis.

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inSARa: Intuitive and Interactive SAR Interpretation by Reduced Graphs and Hierarchical MCS-Based Network Navigation

Cited by 12 publications

References 72 publications

ChemTreeMap: an interactive map of biochemical similarity in molecular datasets

ChemTreeMap: an interactive map of biochemical similarity in molecular datasets

Navigating pharmacophore space to identify activity discontinuities: A case study with BCR‐ABL

Hydrogen Bonding in 1‐Propanol‐Ethanol Binary Mixture: Experimental and Modeling Approaches

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