QSAR are commonly regarded as the best approaches to computational molecular design.

因此QSAR一般被公认为是电脑辅助药物设计中最好的方法.

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Data mining and quantitative structure - activity relationship ( QSAR ) are fundamental approaches for drug discoverty research.

数据发掘和定量的构效关系 ( QSAR ) 是药物发现中重要的研究方法.本文首先介绍了一种极具实用价值的无模式分类与模式识别的基本方法,即k最近邻法.

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Compared with other QSAR methods, EVA possesses several advantages, especially that it does not need alignment.

与其它的定量构效关系方法相比, EVA具有 很多优点, 尤其是不需要分子叠合,这使得EVA活性预测过程自动而快速.

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