Identification of structural similarity of organic molecules

Takahashi, Yoshimasa

doi:10.1007/3-540-58672-5_25

Cited by 7 publications

(4 citation statements)

References 52 publications

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“…Many of works are based on structural similarity analysis. Structural similarity provides us a lot of information on structure-activity and structure-property problems [1,2], and for the selection of candidate analogs as new chemicals [3][4][5]. The basic idea behind them is that structurally similar compounds are likely to possess similar molecular properties and similar biological activities.…”

Section: Introductionmentioning

confidence: 99%

Identification of Dopamine D1 Receptor Agonists and Antagonists under Existing Noise Compounds by TFS-based ANN and SVM

Takahashi

Fujishima

Nishikoori

et al. 2005

J. Comput. Chem. Jpn.

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This paper describes classification and prediction for pharmacologically active classes of drugs under the presence of noise chemical compounds. Dopamine D1 receptor agonists (63 compounds), antagonists (169 compounds) and other drugs (696 compounds) were used for the work. Each drug molecule was characterized with Topological Fragment Spectra (TFS) reported by the authors. TFS-based artificial neural network (TFS/ANN) and support vector machine (TFS/SVM) were employed and evaluated for their classification and prediction abilities. It was concluded that the TFS/SVM works better than TFS/ANN in both the training and the prediction.

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Section: Introductionmentioning

confidence: 99%

Identification of Dopamine D1 Receptor Agonists and Antagonists under Existing Noise Compounds by TFS-based ANN and SVM

Takahashi

Fujishima

Nishikoori

et al. 2005

J. Comput. Chem. Jpn.

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“…This is true in chemistry. The use of molecular similarity methods, especially structural similarity, provides us with a lot of information on structure−activity and structure−property problems. , And it is still under active development in the area of drug design, for the selection of candidate analogues as new chemicals and for the estimation of molecular properties. − The basic idea behind them is that structurally similar compounds are likely to possess similar molecular properties and similar biological activities. Most of the approaches for the evaluation are based on finding particular functional atoms or atomic groups defined in advance.…”

Section: Introductionmentioning

confidence: 99%

Classification of Dopamine Antagonists Using TFS-Based Artificial Neural Network

Fujishima

Takahashi

2004

J. Chem. Inf. Comput. Sci.

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In the former work, the authors proposed the Topological Fragment Spectral (TFS) method as a tool for the description of the topological structure profile of a molecule. This paper describes the TFS-based artificial neural network (TFS/ANN) approach for the classification and the prediction of pharmacological active classes of chemicals. Dopamine antagonists of 1227 that interact with different types of receptors (D1, D2, D3, and D4) were used for the training. The TFS/ANN successfully classified 89% of the drugs into their own active classes. Then, the trained model was used for predicting the class of unknown compounds. For the prediction set of 137 drugs that were not included in the training set, the TFS/ANN model predicted 111 (81%) drugs of them into their own active classes correctly.

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“…Such algorithms have been widely used in chemical information retrieval systems and retrosynthetic analyses. [22][23][24][25][26][27] We describe in this paper a new program, Stigmata, which was developed to find structural commonalities in sets of chemical compounds. Stigmata differs from maximal common subgraph algorithms by incorporating flexibility to allow the detection of commonalities which may exist only in fractions of the dataset.…”

Section: Introductionmentioning

confidence: 99%

“…Algorithms which search for common substructures, for example, usually require that the substructures exist in all structures in the dataset. Such algorithms have been widely used in chemical information retrieval systems and retrosynthetic analyses. − …”

Section: Introductionmentioning

confidence: 99%

Stigmata: An Algorithm To Determine Structural Commonalities in Diverse Datasets

Shemetulskis¹,

Weininger²,

Blankley³

et al. 1996

J. Chem. Inf. Comput. Sci.

102

104

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An algorithm, Stigmata, is described, which extracts structural commonalities from chemical datasets. It is discussed using several illustrative examples and a pharmaceutically interesting set of dopamine D2 agonists. The commonalities are determined using two-dimensional topological chemical descriptions and are incorporated into the key feature of the algorithm, the modal fingerprint. Flexibility is built into the algorithm by means of a user-defined threshold value, which affects the information content of the modal fingerprint. The use of the modal fingerprint as a diversity assessment tool, as a database similarity query, and as a basis for color mapping the determined commonalities back onto the chemical structures is demonstrated.

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Identification of structural similarity of organic molecules

Cited by 7 publications

References 52 publications

Identification of Dopamine D1 Receptor Agonists and Antagonists under Existing Noise Compounds by TFS-based ANN and SVM

Identification of Dopamine D1 Receptor Agonists and Antagonists under Existing Noise Compounds by TFS-based ANN and SVM

Classification of Dopamine Antagonists Using TFS-Based Artificial Neural Network

Stigmata: An Algorithm To Determine Structural Commonalities in Diverse Datasets

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