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バイオインフォマティクス推進センター事業(BIRD) 第8回研究開発成果報告会 知識発見への挑戦 〜バイオインフォマティクスの飛躍に向けて〜 要旨集  10-15
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榊原 康文1)
1) 慶應義塾大学理工学部

Since tens of millions of chemical compounds have been accumulated in public chemical databases such as PubChem, fast comprehensive computational systems to predict interactions between chemical compounds and proteins are demanded for virtual screening of lead compounds. We have implemented a software system COPICAT for predicting protein-chemical interaction using two-layer Support Vector Machine (SVM) classifiers. Further, COPICAT Web System performs a prediction job against database which receives user compound or protein data as a query and returns prediction results against all molecules in one of system-side databases. Our fast and accurate in silico virtual screening system, COPICAT, enhances lead compound discovery against tens of millions of chemical compounds, implying that the search space for drug discovery is extended by more than 1,000 times compared with currently well-used high-throughput screening methodologies.

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