Benefit of Retraining pKa Models Studied Using Internally Measured Data
Benefit of Retraining pKa Models Studied Using Internally Measured Data
复制标题
使用内部测量数据研究的再训练 pKa 模型的好处
DOI：
10.1021/acs.jcim.5b00172
复制
发表时间：
2015
影响因子：
5.6
通讯作者：
F. Lombardo
中科院分区：
文献类型：
--
作者：
P. Gedeck;Yipin Lu;S. Skolnik;S. Rodde;G. Dollinger;Weiping Jia;Giuliano Berellini;R. Vianello;B. Faller;F. Lombardo
药物的电离状态影响许多药物性质，例如它们的溶解性、渗透性和生物活性。因此，在电极导线优化过程中，了解酸碱解离常数pKa的结构特性关系非常重要，以便做出更明智的设计决策。计算方法，例如在MoKa中实现的方法，可以帮助实现这一点;然而，它们通常预测误差过大，特别是对于专有化合物。在这篇文章中，我们研究了再训练如何有助于大大改善预测误差。使用在药物发现环境中测量超过15年的数据的纵向研究，我们评估了模型训练对预测准确性的影响，并研究了模型随时间的退化。使用MoKa软件，我们将证明需要定期再培训，以解决导致模型退化的化学空间变化超过6至9个月。
The ionization state of drugs influences many pharmaceutical properties such as their solubility, permeability, and biological activity. It is therefore important to understand the structure property relationship for the acid-base dissociation constant pKa during the lead optimization process to make better-informed design decisions. Computational approaches, such as implemented in MoKa, can help with this; however, they often predict with too large error especially for proprietary compounds. In this contribution, we look at how retraining helps to greatly improve prediction error. Using a longitudinal study with data measured over 15 years in a drug discovery environment, we assess the impact of model training on prediction accuracy and look at model degradation over time. Using the MoKa software, we will demonstrate that regular retraining is required to address changes in chemical space leading to model degradation over six to nine months.