基于神经网络并使用原子5片段描述符预测正辛醇-水分配系数
A neural network based prediction of octanol-water partition coefficients using atomic5 fragmental descriptors.
作者信息
Molnár László, Keseru György M, Papp Akos, Gulyás Zsolt, Darvas Ferenc
机构信息
Department of Chemical Information Technology, Budapest University of Technology and Economics, Szent Gellért tér 4., H-1111 Budapest, Hungary.
出版信息
Bioorg Med Chem Lett. 2004 Feb 23;14(4):851-3. doi: 10.1016/j.bmcl.2003.12.024.
An artificial neural network based approach using Atomic5 fragmental descriptors has been developed to predict the octanol-water partition coefficient (logP). We used a pre-selected set of organic molecules from PHYSPROP database as training and test sets for a feedforward neural network. Results demonstrate the superiority of our non-linear model over the traditional linear method.
已开发出一种基于人工神经网络的方法，该方法使用Atomic5片段描述符来预测正辛醇-水分配系数（logP）。我们从PHYSPROP数据库中预先选择了一组有机分子作为前馈神经网络的训练集和测试集。结果表明，我们的非线性模型优于传统的线性方法。
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