효소 반응 예측을 위한 유사도 모델 분석 및 구현

초록

With the beginning of the new era of bigdata, information extraction or prediction are an important research area. Here, we present the acquisition of semi-automatically curated large-scale biological database and the prediction of enzyme reaction annotation for analyzing the pharmacological activities of drugs. Because the xenobiotic metabolism of pharmaceutical drugs by cellular enzymes is an important aspect of pharmacology and medicine. In this study, we apply and analyze similarity models to predict bimolecular reactions between human enzymes and their corresponding substrates. Thirteen models select to reflect the characteristics of each cluster in the similarity model. These models compare based on sensitivity and AUC. Among the evaluation models, the Simpson coefficient model showed the best performance in predicting the reactivity between the enzymes. The whole similarity model implement as a web service. The proposed model can respond dynamically to the addition of reaction information, which will contribute to the shortening of new drug development time and cost reduction.

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