Artificial Neural Network Based Analysis of High Throughput Screening Data for Improved Prediction of Active Compounds
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Issue Date
2009-12-14Author
Chakrabarti, Swapan
Svojanovsky, Stan R.
Slavik, Romana
Georg, Gunda I.
Wilson, George S.
Smith, Peter G.
Publisher
SAGE Publications
Type
Article
Article Version
Scholarly/refereed, author accepted manuscript
Rights
Copyright SAGE Publications
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Show full item recordAbstract
Artificial Neural Networks (ANNs) are trained using High Throughput Screening (HTS) data to recover active compounds from a large data set. Improved classification performance was obtained on combining predictions made by multiple ANNs. The HTS data, acquired from a Methionine Aminopeptidases Inhibition study, consisted of a library of 43,347 compounds, and the ratio of active to non-active compounds, RA/N, was 0.0321. Back-propagation ANNs were trained and validated using Principal Components derived from the physico-chemical features of the compounds. On selecting the training parameters carefully, an ANN recovers one-third of all active compounds from the validation set with a three-fold gain in RA/N value. Further gains in RA/N values were obtained upon combining the predictions made by a number of ANNs. The generalization property of the back-propagation ANNs was utilized to train those ANNs with the same training samples, after being initialized with different sets of random weights. As a result, only 10% of all available compounds were needed for training and validation, and the rest of the data set was screened with more than a ten-fold gain of the original RA/N value. Thus, ANNs trained with limited HTS data might become useful in recovering active compounds from large data sets.
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Citation
Chakrabarti, Swapan, Stan R. Svojanovsky, Romana Slavik, Gunda I. Georg, George S. Wilson, and Peter G. Smith. "Artificial Neural Networkâ Based Analysis of High-Throughput Screening Data for Improved Prediction of Active Compounds." Journal of Biomolecular Screening 14.10 (2009): 1236-244.
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