Topological torsion: a new molecular descriptor for SAR applications. Comparison with other descriptors

Nilakantan, Ramaswamy; Bauman, Norman; Dixon, Jonathan S.

doi:10.1021/ci00054a008

Cited by 300 publications

(239 citation statements)

References 2 publications

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“…ChemTreeMap also calculates atom-pairs fingerprints (Carhart et al, 1985) as an alternative metric. Others can be added easily, such as MACCS keys (Durant et al, 2002), topological torsion fingerprints (Nilakantan et al, 1987) and 2D-pharmacophore fingerprints (Gobbi and Poppinger, 1998).…”

Section: Representation Of the Moleculesmentioning

confidence: 99%

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: Representation Of the Moleculesmentioning

confidence: 99%

ChemTreeMap: an interactive map of biochemical similarity in molecular datasets

Lü¹,

Carlson

2016

Bioinformatics

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“…Daylight fingerprint [14] Atom pair [15] Topological torsion [16] BCUT [17] [25] 114,501 643 TFS M Table 1 Table 2 5 M x y …”

Section: Tfsmentioning

confidence: 99%

Compression of Topological Fragment Spectra (TFS) for Accelerating Chemical Data Mining

Shiga¹,

Takahashi²

2012

J. Comput. Chem. Jpn.

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The chemical space of drug candidates is vast, and data volume in chemical databases is still getting larger. For mining such vast chemical data, accelerating data analysis is an essential issue. This paper validates an approach for improving computational cost by compressing topological fragment spectra (TFS) which is a descriptor of chemical graphs proposed by Takahashi et al.. First we show that TFS is a periodic signal whose cycle length is around 12 (mass number of carbon). And then we apply compression methods for periodic signals: Fourier transform and Wavelet transform. Experimental results on structural similarity searches and pharmaceutical activity predictions show that Wavelet transform gives more effective compression than Fourier transform.

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“…There are, essentially, two major types of molecular descriptors [10] . The first of them has been defined as "holistic".…”

Section: Introductionmentioning

confidence: 99%

“…Examples that could be mentioned among many others are: octanol-water partition coefficient [11] , Kier Shape Index [12] , the shape indices of Hopfinger [13] and the Index of Electrotopological state [14] . The other descriptor category consists of the so called chemical substructures [1,10] . These are pieces of information strung together that may include atomic species, bond types and connectivity of two or more atoms.…”

Section: Introductionmentioning

confidence: 99%

“…They could be seen as subgraphs of the graph that is associated to a molecular structure, labeled or colored in a way that reflects the information mentioned above. Some examples that fall in this category are: Atom Pairs (AP) [15,16,1] , Topological Torsions (TT) [1,10] and Atom Sequences (AS) [16] . Such descriptors are particularly useful in retrieval systems, data analysis and compound classification [17,18] .…”

Section: Introductionmentioning

confidence: 99%

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On Application of Carhart Atom Pairs to Predict Anticonvulsant Activity

Talevi¹,

Prieto²,

Bruno-Blanch³

et al. 2006

American J. of Biochemistry and Biotechnology

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About 50 million people in the world suffer from epilepsy, especially in childhood, adolescence and old age. Available treatment fails to control epilepsy in up to 30% of affected people. In developing countries, however, the amount of patients that do not receive adequate treatment climbs up to 75%. Moreover, the new generation of antiepileptic drugs (AEDs) causes important central and peripheral side effects, including ataxia, diplopia, dizziness, headache, nausea, allergies and sedation. A mathematical model previously developed by Bagchi and Maiti, involving Carhart atom pairs and similarity measures, is applied in the prediction of anticonvulsant activity of two sets of compounds which have shown to be active in the Maximal Electroshock Seizure (MES) test, meaning that their mechanism of action can be at least partially explained through sodium channels blockade. Nine structurally heterogeneous molecules define the first set of compounds, with Carhart similarities to carbamazepine ranging from 0.005 to 0.593. The second set is defined by four benzodiazepines derivatives with Carhart similarities to THIQ-10c ranging from 0.533 to 0.570. A new, more specific, model is constructed based on the one from Bagchi and Maiti and a pharmacophore previously identified in our laboratory through an active analog approach. Applied to both sets of compounds, our model shows smaller average percentage error and average absolute error in the prediction than the one form Bagchi and Maiti and smaller SD as well. Accuracy and precision in the prediction also increases compared to those obtained when using bare similarity coefficients as relative activity indicators.

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Topological torsion: a new molecular descriptor for SAR applications. Comparison with other descriptors

Cited by 300 publications

References 2 publications

ChemTreeMap: an interactive map of biochemical similarity in molecular datasets

ChemTreeMap: an interactive map of biochemical similarity in molecular datasets

Compression of Topological Fragment Spectra (TFS) for Accelerating Chemical Data Mining

On Application of Carhart Atom Pairs to Predict Anticonvulsant Activity

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