Learning the next generation of drug targets by modelling diseases, targets and their relationships

通过对疾病、靶点及其关系进行建模来学习下一代药物靶点

• 批准号: 2281344
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2281344
• 关键词: Learning; next; generation; drug; targets

Target selection is the first and arguably most important decision in any drug discovery programme. Drug development is a lengthy, costly process with remarkably low success rates, and a substantial amount of failures is due to poor target selection strategies. In this proposed project we aim to tackle this issue by leveraging the wealth of biomedical data available in the public domain and state-of-the art data mining and machine learning methods. The key deliverables of the project will be (1) identification of features defining successful drug targets; (2) discovery of novel candidate targets; (3) publication of results. The project will be aligned to the "Biological informatics" and the "Artificial intelligence technologies" research areas of the EPSRC.

靶点选择是任何药物发现计划中的首要决定，也可以说是最重要的决定。药物开发是一个漫长、昂贵的过程，成功率极低，大量失败是由于靶标选择策略不佳造成的。在这个拟议的项目中，我们的目标是通过利用公共领域中丰富的生物医学数据以及最先进的数据挖掘和机器学习方法来解决这个问题。该项目的主要成果是（1）确定成功药物靶标的特征； (2) 发现新的候选靶点； （三）结果公布。该项目将与 EPSRC 的“生物信息学”和“人工智能技术”研究领域保持一致。