Machine learning methods for complex molecular structural data: From self-assembly and folding to macromolecular assemblies.

复杂分子结构数据的机器学习方法：从自组装和折叠到大分子组装。

• 批准号: 2434910
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2434910
• 关键词: Machine; learning; methods; complex; molecular

Structural data underpins the understanding of fundamental biological functions, but is often highly complex, based on the size of bioactive molecules. Simulations can provide some insight, but additionally generate a substantial quantity of temporal data. To tackle these issues, transformative approaches are required. Machine-learning allows us to reduce data sets to key features to provide new insights on structural relationships. We will develop new methodologies to tackle areas such as the folding of mycolic acids, found in tuberculosis and related pathogens, and the interactions of proteins for biological regulation, that provide enhanced ways of rapidly understanding and ascribing relevant functional information to these important systems.

结构数据支持对基本生物功能的理解，但基于生物活性分子的大小，结构数据往往非常复杂。模拟可以提供一些洞察力，但另外还会生成大量的时间数据。要解决这些问题，需要采取变革性的方法。机器学习使我们能够将数据集减少到关键特征，以提供关于结构关系的新见解。我们将开发新的方法来处理一些领域，如在结核病和相关病原体中发现的霉菌酸的折叠，以及用于生物调节的蛋白质之间的相互作用，这些方法提供了快速理解和将相关功能信息归因于这些重要系统的增强方式。