Al-Fahemi Jabir H A, Cooper David L, Allan Neil L
Department of Chemistry, University of Liverpool, Liverpool L69 7ZD, UK.
J Mol Graph Model. 2007 Oct;26(3):607-12. doi: 10.1016/j.jmgm.2007.01.002. Epub 2007 Jan 14.
We investigate the possible use of families of momentum-space descriptors and of trivial classical descriptors for the prediction of blood-brain barrier penetration, expressed as log BB. A 12-descriptor model based on entropy-like momentum-space quantities and on the numbers of atoms of each type has good statistical quality for a set of 42 structurally diverse molecules. We also consider the inclusion in our models of some of the other descriptors that have been used in earlier models for these molecules. The resulting models are not expected to be useful as-is for making genuine predictions for much larger test sets, but the various results do demonstrate the potential benefits of incorporating momentum-space descriptors into QSAR models for predicting logBB.
我们研究了动量空间描述符族和简单经典描述符用于预测血脑屏障渗透率（以log BB表示）的可能性。基于类熵动量空间量和每种类型原子数的12描述符模型，对于一组42个结构多样的分子具有良好的统计质量。我们还考虑将早期模型中用于这些分子的一些其他描述符纳入我们的模型。所得模型按原样对更大的测试集进行实际预测时预计不会有用，但各种结果确实证明了将动量空间描述符纳入QSAR模型以预测logBB的潜在益处。
