Tetko Igor V, Bruneau Pierre
Biomedical Department, IBPC, Ukrainian Academy of Sciences, Murmanskaya 1, Kyiv, 02094, Ukraine.
J Pharm Sci. 2004 Dec;93(12):3103-10. doi: 10.1002/jps.20217.
The ALOGPS 2.1 was developed to predict 1-octanol/water partition coefficients, logP, and aqueous solubility of neutral compounds. An exclusive feature of this program is its ability to incorporate new user-provided data by means of self-learning properties of Associative Neural Networks. Using this feature, it calculated a similar performance, RMSE = 0.7 and mean average error 0.5, for 2569 neutral logP, and 8122 pH-dependent logD(7.4), distribution coefficients from the AstraZeneca "in-house" database. The high performance of the program for the logD(7.4) prediction looks surprising, because this property also depends on ionization constants pKa. Therefore, logD(7.4) is considered to be more difficult to predict than its neutral analog. We explain and illustrate this result and, moreover, discuss a possible application of the approach to calculate other pharmacokinetic and biological activities of chemicals important for drug development.
ALOGPS 2.1用于预测中性化合物的1-辛醇/水分配系数logP和水溶性。该程序的一个独特功能是能够通过关联神经网络的自学习特性整合用户提供的新数据。利用此功能，对于来自阿斯利康“内部”数据库的2569个中性logP和8122个pH依赖性logD(7.4)（分配系数），它计算出了相似的性能，均方根误差RMSE = 0.7，平均误差为0.5。该程序在logD(7.4)预测方面的高性能看起来令人惊讶，因为此属性还取决于电离常数pKa。因此，logD(7.4)被认为比其中性类似物更难预测。我们解释并说明了这一结果，此外，还讨论了该方法在计算对药物开发重要的其他化学物质的药代动力学和生物活性方面的可能应用。
