Kunimoto Ryo, Miyao Tomoyuki, Bajorath Jürgen
Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität Dahlmannstr. 2 D-53113 Bonn Germany
RSC Adv. 2018 Jan 31;8(10):5484-5492. doi: 10.1039/c7ra13748f. eCollection 2018 Jan 29.
In lead optimization, it is difficult to estimate when an analog series might be saturated and synthesis of additional compounds would be unlikely to yield further progress. Rather than terminating a series, one often continues to generate analogs hoping to reach the final optimization goal, even if obstacles that are faced ultimately prove to be unsurmountable. Clearly, methodologies to better understand series progression and saturation are highly desirable. However, only a few approaches are currently available to monitor series progression and aid in decision making. Herein, we introduce a new computational method to assess progression saturation of an analog series by relating the properties of existing compounds to those of synthetic candidates and comparing their distributions in chemical space. The neighborhoods of analogs are analyzed and the distance relationships between existing and candidate compounds quantified. An intuitive dual scoring scheme makes it possible to characterize analog series and their degree of progression saturation.
在先导化合物优化过程中，很难估计一个类似物系列何时可能达到饱和，以及合成更多化合物是否不太可能带来进一步进展。与其终止一个系列，人们通常会继续生成类似物，希望达到最终的优化目标，即使最终面临的障碍被证明是无法克服的。显然，非常需要能够更好地理解系列进展和饱和情况的方法。然而，目前只有少数方法可用于监测系列进展并辅助决策。在此，我们介绍一种新的计算方法，通过将现有化合物的性质与合成候选物的性质相关联，并比较它们在化学空间中的分布，来评估类似物系列的进展饱和度。分析类似物的邻域，并量化现有化合物与候选化合物之间的距离关系。一种直观的双重评分方案使得能够表征类似物系列及其进展饱和程度。
