Machine learning for drug target identification

用于药物靶点识别的机器学习

• 批准号: 2223129
• 金额: --
• 依托单位: University of Dundee
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2223129
• 关键词: Machine; learning; drug; target; identification

Traditionally, in drug discovery, target identification to develop novel agents for any given disease is carried out on a case-by-case basis. In this model, individual scientists act as project champions for targets based on available literature and local expertise. This approach has weaknesses n that it is ad-hoc in nature and it is almost impossible to maintain an overview over an increasingly vast scientific literature. There are efforts underway in the pharmaceutical industry to apply a more systemic approach (for instance, OpenTargets initiative). This PhD iCASE studentship will focus on the development of an innovative integrated resource, which uses machine learning to facilitate a systematic or genome scale ranking of potential targets, and will build on the School of Medicine expertise in the areas of type 2 diabetes and metabolic syndrome.Project provides training in Interdisciplinary & Quantitative Skills.

传统上，在药物发现中，为任何特定疾病开发新药的目标识别是在个案的基础上进行的。在这种模式中，个别科学家充当基于现有文献和当地专业知识的目标的项目冠军。这种方法有缺点，因为它本质上是临时的，几乎不可能对日益庞大的科学文献进行概述。制药业正在努力采用更系统的方法(例如，OpenTarget计划)。这个博士生项目将专注于开发一种创新的综合资源，它使用机器学习来促进对潜在目标的系统或基因组规模的排名，并将建立在医学院在2型糖尿病和代谢综合征领域的专业知识的基础上。该项目提供跨学科和定量技能的培训。