Classification of Dopamine Antagonists Using TFS‐Based Artificial Neural Network.

Fujishima, Satoshi; Takahashi, Yoshimasa

doi:10.1002/chin.200430222

Cited by 2 publications

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“…We investigated the usage of SVM and ANN for drug discovery too. In a previous work, we reported that an ANN approach combined with the topological fragment spectra (TFS) allowed us to successfully classify dopamine antagonists that interact with four different types of dopamine receptors, and it could be applied to the prediction of activity for class-unknown compounds. It was also shown that SVM works for this type of problem – much better.…”

Section: Introductionmentioning

confidence: 99%

Predictive Activity Profiling of Drugs by Topological-Fragment-Spectra-Based Support Vector Machines

Kawai

Fujishima

Takahashi

2008

J. Chem. Inf. Model.

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Aiming at the prediction of pleiotropic effects of drugs, we have investigated the multilabel classification of drugs that have one or more of 100 different kinds of activity labels. Structural feature representation of each drug molecule was based on the topological fragment spectra method, which was proposed in our previous work. Support vector machine (SVM) was used for the classification and the prediction of their activity classes. Multilabel classification was carried out by a set of the SVM classifiers. The collective SVM classifiers were trained with a training set of 59,180 compounds and validated by another set (validation set) of 29,590 compounds. For a test set that consists of 9,864 compounds, the classifiers correctly classified 80.8% of the drugs into their own active classes. The SVM classifiers also successfully performed predictions of the activity spectra for multilabel compounds.

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Section: Introductionmentioning

confidence: 99%

Predictive Activity Profiling of Drugs by Topological-Fragment-Spectra-Based Support Vector Machines

Kawai

Fujishima

Takahashi

2008

J. Chem. Inf. Model.

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“…In our preceding works [10], we reported that an artificial neural network (ANN) approach combined with the TFS as input signals allowed us to successfully classify the type of activities for dopamine receptor antagonists that interact with four different types of dopamine receptors, and that it could be applied to the prediction of active class for the test compounds. It was also shown that the support vector machine (SVM) works for this problem much better [11].…”

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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Classification of Dopamine Antagonists Using TFS‐Based Artificial Neural Network.

Cited by 2 publications

References 1 publication

Predictive Activity Profiling of Drugs by Topological-Fragment-Spectra-Based Support Vector Machines

Predictive Activity Profiling of Drugs by Topological-Fragment-Spectra-Based Support Vector Machines

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

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