Development of new computational and experimental methods for fragment based drug discovery by NMR

许多处方药产生不良反应，缺乏疗效或依从性差。精准医疗概念的核心是药物功效最大化和副作用最小化。由于“孟德尔随机化”，观察性研究可以提供基因变异对治疗反应影响的准确估计。英国初级保健数据为这种观察性药物遗传学研究提供了独特的机会。利用来自当地电子健康、环境和DNA扩展研究（EXCEED）队列的数据，学生将开发和验证研究方法，这些方法可以识别英国初级保健中使用的电子医疗记录中的不良反应或缺乏疗效的特征。然后，这些将用于利用大型电子医疗记录研究（包括UK Biobank）中的初级保健数据进行强有力的药物遗传学研究。该学生将在一个国际领先的遗传流行病学小组工作，该小组有培养获奖学生的记录。该项目将成为遗传流行病学高级培训的极好工具。申请人应具有统计学，计算生物学，生物信息学或相关领域的背景，并具有流行病学知识和/或经验，并具有良好的计算机编程技能。

Many prescribed drugs generate adverse effects, lack efficacy or are poorly adhered to. Central to the concept of precision medicine is the maximisation of drug efficacy and the minimisation of side effects. Due to "Mendelian randomisation" observational studies can provide unconfounded estimates of the effects of genetic variants on treatment response. UK primary care data provides unique opportunities for such observational pharmacogenetic studies. Working with data from the local Extended Study of E-health, Environment and DNA (EXCEED) cohort, the student will develop and validate research methods which can identify signatures of adverse reactions or lack of efficacy in electronic medical records employed in UK primary care. These will then be utilised to undertake well powered pharmacogenetic studies using primary care data in large electronic medical record based studies, including UK Biobank. The student will be based in an internationally leading genetic epidemiology group with a track record of training award winning students. The project will be an excellent vehicle for advanced training in genetic epidemiology. Applicants should have a background in statistics, computational biology, bioinformatics or closely related area coupled with knowledge and/or experience of epidemiology and good computer programming skills.