Department of Life Science Informatics Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Endenicher Allee 19c, D-53115, Bonn, Germany.
Mol Inform. 2020 Dec;39(12):e2000046. doi: 10.1002/minf.202000046. Epub 2020 Apr 24.
In medicinal chemistry, compound optimization largely depends on chemical knowledge, experience, and intuition, and progress in hit-to-lead and lead optimization projects is difficult to estimate. Accordingly, approaches are sought after that aid in assessing the odds of success with an optimization project and making decisions whether to continue or discontinue work on an analog series at a given stage. However, currently there are only very few approaches available that are capable of providing decision support. We introduce a computational methodology designed to combine the assessment of chemical saturation of analog series and structure-activity relationship (SAR) progression. The current endpoint of these development efforts, the compound optimization monitor (COMO), further extends lead optimization diagnostics to compound design and activity prediction. Hence, COMO plays dual role in supporting lead optimization campaigns.
在药物化学中,化合物优化在很大程度上依赖于化学知识、经验和直觉,并且很难估计命中到先导化合物优化和先导化合物优化项目的进展。因此,人们一直在寻找能够帮助评估优化项目成功几率并决定是否继续或停止在给定阶段进行类似物系列工作的方法。然而,目前只有极少数能够提供决策支持的方法。我们引入了一种计算方法,旨在结合类似物系列化学饱和度和构效关系(SAR)进展的评估。这些开发工作的当前终点——化合物优化监测器(COMO),进一步将先导化合物优化诊断扩展到化合物设计和活性预测。因此,COMO 在支持先导化合物优化活动方面发挥着双重作用。