Warner Daniel J, Bridgland-Taylor Matthew H, Sefton Clare E, Wood David J
Department of Medicinal Chemistry, AstraZeneca R&D Montreal, Montreal, Quebec, H4S 1Z9, Canada.
Department of Safety Pharmacology, Safety Assessment UK, AstraZeneca Pharmaceuticals, Alderley Park, Macclesfield, SK10 4TG, UK.
Mol Inform. 2012 May;31(5):365-8. doi: 10.1002/minf.201200020. Epub 2012 Apr 30.
Matched molecular pairs analysis (MMPA)1,2 is an inverse quantitative structure activity relationship (QSAR) technique that is rapidly gaining popularity in the retrospective analysis of large experimental datasets.3,4 While much of the recent focus has been on the differences in properties between structurally related groups of existing compounds, attempts to extend this methodology to the de-novo design of novel structures have been limited. To our knowledge the aggregate effect of multiple transformations, all suggesting the same molecular structure, has only ever being considered within a very limited dataset.5 We therefore sought to test this exciting new approach to the design (and absolute property prediction - effectively QSAR-by-MMPA) of novel chemical entities based on a larger, more diverse dataset, and couple these designs to MMPA-based predictions of antitarget activity.
匹配分子对分析(MMPA)1,2是一种反向定量构效关系(QSAR)技术,在大型实验数据集的回顾性分析中迅速受到欢迎。3,4虽然最近的大部分重点都放在现有化合物结构相关组之间性质的差异上,但将这种方法扩展到新结构的从头设计的尝试却很有限。据我们所知,多种转化的综合效应,所有这些都表明相同的分子结构,仅在非常有限的数据集中被考虑过。5因此,我们试图基于更大、更多样化的数据集测试这种用于设计新型化学实体(以及绝对性质预测——有效地通过MMPA进行QSAR)的令人兴奋的新方法,并将这些设计与基于MMPA的抗靶标活性预测相结合。