Keyes Philip, Hernandez Gonzalo, Cianchetta Giovanni, Robinson James, Lefebvre Brent
Lexicon Pharmaceuticals, Analytical Chemistry, 350 Carter Road, Princeton, New Jersey, 08540, USA.
Magn Reson Chem. 2009 Jan;47(1):38-52. doi: 10.1002/mrc.2347.
Since the introduction of NMR prediction software, medicinal chemists have imagined submitting their compounds to corporate compound registration systems that would ultimately display a simplified pass/fail result. We initially implemented such a system based on HPLC and liquid chromatography mass spectrometry (LCMS) data that is embedded within our industry standard sample submission and registration process. By using gradient-heteronuclear single quantum coherence (HSQC) experiments, we have extended this concept to NMR data through a comparison of experimentally acquired data against predicted (1)H and (13)C NMR data. Integration of our compound registration system with our analytical instruments now provides our chemists unattended and automated NMR verification for collections of submitted compounds. The benefits achieved from automated processing and interpretation of results produced enhanced confidence in our compound library and released the chemists from the tedium of manipulating large amounts of data. This allows scientists to focus more of their attention to the drug discovery process.
自从核磁共振(NMR)预测软件问世以来,药物化学家们就设想将他们的化合物提交到公司化合物注册系统中,最终该系统能显示一个简化的通过/不通过结果。我们最初基于高效液相色谱(HPLC)和液相色谱-质谱联用(LCMS)数据实施了这样一个系统,这些数据嵌入在我们的行业标准样品提交和注册流程中。通过使用梯度异核单量子相干(HSQC)实验,我们通过将实验获取的数据与预测的氢(¹H)和碳(¹³C)核磁共振数据进行比较,将这一概念扩展到了核磁共振数据。我们的化合物注册系统与分析仪器的整合,现在为提交的化合物集合为我们的化学家提供了无人值守且自动化的核磁共振验证。自动处理和结果解读所带来的好处增强了我们对化合物库的信心,并使化学家们从处理大量数据的繁琐工作中解脱出来。这使科学家们能够将更多注意力集中在药物发现过程上。