Department of Chemistry, University of Waterloo, Waterloo, N2L 3G1, ON, Canada.
SCIEX, 71 Four Valley Drive, Concord, L4V 4V8, ON, Canada.
Nat Commun. 2018 Nov 30;9(1):5096. doi: 10.1038/s41467-018-07616-w.
The fast and accurate determination of molecular properties is highly desirable for many facets of chemical research, particularly in drug discovery where pre-clinical assays play an important role in paring down large sets of drug candidates. Here, we present the use of supervised machine learning to treat differential mobility spectrometry - mass spectrometry data for ten topological classes of drug candidates. We demonstrate that the gas-phase clustering behavior probed in our experiments can be used to predict the candidates' condensed phase molecular properties, such as cell permeability, solubility, polar surface area, and water/octanol distribution coefficient. All of these measurements are performed in minutes and require mere nanograms of each drug examined. Moreover, by tuning gas temperature within the differential mobility spectrometer, one can fine tune the extent of ion-solvent clustering to separate subtly different molecular geometries and to discriminate molecules of very similar physicochemical properties.
快速准确地确定分子性质在化学研究的许多方面都是非常理想的，特别是在药物发现中，临床前测定在缩小大量候选药物方面发挥着重要作用。在这里，我们介绍了使用监督机器学习来处理十种拓扑类别的药物候选物的差分迁移率谱 - 质谱数据。我们证明，我们实验中探测到的气相团聚行为可用于预测候选物的凝聚相分子性质，如细胞通透性、溶解度、极性表面积和水/辛醇分配系数。所有这些测量都可以在几分钟内完成，并且仅需检查的每种药物的纳米克数。此外，通过在差分迁移率谱仪内调节气体温度，可以精细调节离子 - 溶剂团聚的程度，以分离细微不同的分子几何形状，并区分非常相似的物理化学性质的分子。
