Molecular Shape Descriptors

Arteca, Gustavo A.

doi:10.1002/9780470125861.ch5

Cited by 53 publications

(26 citation statements)

References 210 publications

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“…A study to examine the asphericity of the molecules with respect to the spherical radius (R) was first performed. The asphericity (ASP) constitutes an anisometry descriptor that measures the deviation from the spherical shape [20] ; while the spherical radius of a molecule, as it was mentioned above, is the Euclidean distance between the outermost atom and the geometric center. The ASP values vary between 0 and 1, where values equal to 0 are indicatives of spherical top molecules, while values equal to 1 are indicatives of linear molecules.…”

Section: Relation Between Spherical Radius and Molecular Shapementioning

confidence: 99%

“…Each interval represents a 10% of the length of the spherical radius of the compounds considered, being 0 the geometric center. These intervals are defined as follows: [0,10), [10,20), [20,30), [30,40), [40,50), [50,60), [60,70), [70,80), [80,90) and [90,100]. The total of atoms analyzed corresponds to the number of heavy atoms in the four datasets aforementioned.…”

Section: Relation Between Spherical Radius and Molecular Shapementioning

confidence: 99%

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Smoothed Spherical Truncation based on Fuzzy Membership Functions: Application to the Molecular Encoding

et al. 2019

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A novel spherical truncation method, based on fuzzy membership functions, is introduced to truncate interatomic (or interaminoacid) relations according to smoothing values computed from fuzzy membership degrees. In this method, the molecules are circumscribed into a sphere, so that the geometric centers of the molecules are the centers of the spheres. The fuzzy membership degree of each atom (or aminoacid) is computed from its distance with respect to the geometric center of the molecule, by using a fuzzy membership function. So, the smoothing value to be applied in the truncation of a relation (or interaction) is computed by averaging the fuzzy membership degrees of the atoms (or aminoacids) involved in the relation. This truncation method is rather different from the existing ones, at considering the geometric center for the whole molecule and not only for atom‐groups, as well as for using fuzzy membership functions to compute the smoothing values. A variability study on a set comprised of 20,469 compounds (15,050 drug‐like compounds, 2994 drugs approved, 880 natural products from African sources, and 1545 plant‐derived natural compounds exhibiting anti‐cancerous activity) demonstrated that the truncation method proposed allows to determine molecular encodings with better ability for discriminating among structurally different molecules than the encodings obtained without applying truncation or applying non‐fuzzy truncation functions. Moreover, a principal component analysis revealed that orthogonal chemical information of the molecules is encoded by using the method proposed. Lastly, a modeling study proved that the truncation method improves the modeling ability of existing geometric molecular descriptors, at allowing to develop more robust models than the ones built only using non‐truncated descriptors. In this sense, a comparison and statistical assessment were performed on eight chemical datasets. As a result, the models based on the truncated molecular encodings yielded statistically better results than 12 procedures considered from the literature. It can thus be stated that the proposed truncation method is a relevant strategy for obtaining better molecular encodings, which will be ultimately useful in enhancing the modeling ability of existing encodings both on small‐to‐medium size molecules and biomacromolecules. © 2019 Wiley Periodicals, Inc.

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Section: Relation Between Spherical Radius and Molecular Shapementioning

confidence: 99%

Section: Relation Between Spherical Radius and Molecular Shapementioning

confidence: 99%

Smoothed Spherical Truncation based on Fuzzy Membership Functions: Application to the Molecular Encoding

et al. 2019

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“…The DT identified 4 MDs i.e. A9 (Second Mohar index, TI2), [19,20,36] A60 (Moriguchi octanol-water partition coefficient, MLOGP) [19,20,37], A35 (Asphericity, ASP) [19,20,38] and A6 (Gutman MTI by valence vertex degrees, GMTIV) [19,20,39] as the most important descriptors. DT classified the analogs with an accuracy of N99% in the training set.…”

Section: Resultsmentioning

confidence: 99%

Diverse classification models for anti-hepatitis C virus activity of thiourea derivatives

Khatri

Lather

Мадан

2015

Chemometrics and Intelligent Laboratory Systems

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“…In a related theme, we note that although some previous works could successfully quantify important aspects of geometry of local shape of protein exterior (Arteca, ; Laskowski et al ., ; Peters et al ., ; Liang et al ., ), an algorithm that relates (time‐dependent and context‐dependent) change in the concavity of the local shape to the change of roughness of surface patch that holds this shape could not be found. The reason for this conspicuous absence can probably be attributed to the complex nature of functional dependence that influences the surface roughness of any given patch of protein surface.…”

Section: Measurement Of Change Of Surface Roughness Before and After mentioning

confidence: 94%

Fractal nature of protein surface roughness: a note on quantification of change of surface roughness in active sites, before and after binding

Banerji

Navare²

2013

J of Molecular Recognition

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Year 2010 marked the 25th year since we came to know that roughness of a protein surface has fractal symmetry. Ever since the publication of Lewis and Rees' paper, hundreds of works from a spectrum of perspectives have established that fractal dimension (FD) can be considered as a reliable marker that describes roughness of protein surface objectively. In this article, we introduce readers to the fundamentals of fractals and present categorical biophysical and geometrical reasons as to why FD-based constructs can describe protein surface roughness more accurately. We then review the commonality (and the lack of it) between numerous approaches that have attempted to investigate protein surface with fractal measures, before exploring the patterns in the results that they have produced. Apart from presenting the genealogy of approaches and results, we present an analysis that quantifies the difference in surface roughness in stretches of protein surface containing the active site, before and after binding to ligands, to underline the utility of FD-based measures further. It has been found that surface stretches containing the active site, in general, undergo a significant increment in its roughness after binding. After presenting the entire repertoire of FD-based surface roughness studies, we talk about two yet-unexplored problems where application of FD-based techniques can help in deciphering underlying patterns of surface interactions. Finally, we list the limitations of FD-based constructs and put down several precautions that one must take while working with them.

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Molecular Shape Descriptors

Cited by 53 publications

References 210 publications

Smoothed Spherical Truncation based on Fuzzy Membership Functions: Application to the Molecular Encoding

Smoothed Spherical Truncation based on Fuzzy Membership Functions: Application to the Molecular Encoding

Diverse classification models for anti-hepatitis C virus activity of thiourea derivatives

Fractal nature of protein surface roughness: a note on quantification of change of surface roughness in active sites, before and after binding

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