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

结构数据支撑着对基本生物学功能的理解，但往往是高度复杂的，基于生物活性分子的大小。模拟可以提供一些见解，但也会产生大量的时间数据。为了解决这些问题，需要采取变革性的方法。机器学习使我们能够将数据集简化为关键特征，从而提供关于结构关系的新见解。我们将开发新的方法来解决诸如结核和相关病原体中发现的霉菌酸折叠以及用于生物调控的蛋白质相互作用等领域，这些领域为快速理解和将相关功能信息归因于这些重要系统提供了增强的方法。

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.