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Molecular activity prediction using deep learning software library

机译:使用深度学习软件库进行分子活性预测

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In order to know how work deep learning method in chemoinformatics and bioinformatics problems, we have attempted to predict the molecular activities using the molecular fingerprints (chemical descriptor vectors) provided by the “Merck molecular activity challenge” competition and an open source deep learning library Chainer. Our result has been able to reproduce almost identical increase-decrease tendencies with the correlation Rs2 of the champion group in the competition. GPU performance was also examined and the speed gain were more than 11 times than only CPU computation.
机译:为了了解深度学习方法在化学信息学和生物信息学问题中的工作方式,我们尝试使用“ Merck分子活性挑战”竞赛提供的分子指纹(化学描述符矢量)和开源深度学习库Chainer来预测分子活性。我们的结果已经能够再现与比赛中冠军组的相关性Rs2几乎相同的增减趋势。还检查了GPU性能,其速度增益比仅CPU计算高出11倍以上。

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