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一种用于血脑屏障渗透的递归划分模型。

A recursive-partitioning model for blood-brain barrier permeation.

作者信息

Mente S R, Lombardo F

机构信息

Pfizer Global Research and Development, Groton, CT, USA.

出版信息

J Comput Aided Mol Des. 2005 Jul;19(7):465-81. doi: 10.1007/s10822-005-9001-7. Epub 2005 Dec 6.

Abstract

A series of bagged recursive partitioning models for log(BB) is presented. Using a LGO-CV, three sets of physical property descriptors are evaluated and found to have Q2 values of 0.51 (CPSA), 0.53 (Ro5x) and 0.53 (MOE). Extrapolating these models to Pfizer chemical space is difficult due to P-glycoprotein (P-gp) mediated efflux. Low correlation coefficients for this test set are improved (R2 = 0.39) when compounds known to be P-gp substrates or statistical extrapolations are removed. The use of simple linear models for specific chemical series is also found to improve the correlation over a limited chemical space.

摘要

本文提出了一系列用于log(BB)的袋装递归划分模型。使用LGO交叉验证,评估了三组物理性质描述符,发现其Q2值分别为0.51(CPSA)、0.53(Ro5x)和0.53(MOE)。由于P-糖蛋白(P-gp)介导的外排作用,将这些模型外推到辉瑞化学空间较为困难。当去除已知为P-gp底物的化合物或统计外推数据时,该测试集的低相关系数得到了改善(R2 = 0.39)。还发现,针对特定化学系列使用简单线性模型可在有限的化学空间内提高相关性。

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