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    • 7. 发明授权
    • Method for accurately estimating pKa of molecules using atom type definitions and partial least squares
    • 使用原子类型定义和偏最小二乘法准确估计分子的pKa的方法
    • US07006921B1
    • 2006-02-28
    • US09626099
    • 2000-07-26
    • Li XingRober C. Glen
    • Li XingRober C. Glen
    • G06F19/00
    • G06F19/704
    • A computer implemented software method enables the prediction of the pKa of an arbitrary molecule based upon a knowledge of the molecular structure of that molecule and a statistical analysis of the molecular structures of a group of molecules (training set) for which the pKa is known. Hierarchical atom connectivity trees are constructed for the training set and the various atoms types identified in each molecule are associated in a bit string for that molecule and also associated with the experimentally determined pKa for that molecule. PLS analysis of the training set data yields coefficients associated with each atom type represented in the bit strings. A hierarchical atom connectivity tree may then be constructed for the molecule of interest. The predicted pKa is determined by multiplying the number of occurrences of each atom type in the molecule of interest by the PLS coefficient determined for that atom type and summing the resulting multiplications.
    • 基于对该分子的分子结构的了解和一组分子的分子结构的统计分析(训练集合),计算机实现的软件方法能够预测任意分子的pK a a ),已知pK 。 为训练集合构建分层原子连接性树,并且在每个分子中鉴定的各种原子类型与该分子的位串相关联,并且还与该分子的实验确定的pK 相关联。 训练集数据的PLS分析产生与位串中表示的每个原子类型相关联的系数。 然后可以为感兴趣的分子构建分层的原子连接性树。 通过将感兴趣分子中每个原子类型的出现次数乘以为该原子类型确定的PLS系数并将所得到的乘法相加来确定预测的pK