Analysis and Development of Graph-Embedding Techniques for Biomedical Knowledge Graphs

药物发现过程非常复杂，需要来自各种领域的知识，包括人类疾病，遗传学，结构生物学和药物化学。这些因素促使人们寻找可靠的虚拟（即基于计算机的）筛选方法来选择可能的候选药物。虽然机器学习方法现在已经很好地建立了用于预测小分子的生物活性，但它们通常被应用于预测与单个生物靶标的结合，并且不考虑更复杂的关系，例如副作用的可能性。知识图是一种新的结构，它提供了一种组织来自多个异构源的数据的方法，最近已开始用于药物发现。该项目将开发从知识图中提取信息丰富的特征向量的图论技术，用于机器学习方法。其目的是提供更准确的预测小分子的物理化学和生物化学性质。

The drug discovery process is highly complex requiring knowledge from a variety of domains including human diseases, genetics, structural biological and medicinal chemistry. These factors have prompted the search for reliable virtual (i.e. computer-based) screening methods to select likely drug candidates. While machine learning methods are now well established for the prediction of biological activities of small molecules, they have generally been applied to predict binding to a single biological target and do not consider more complex relationships such as the potential for side effects. Knowledge graphs are a recent construct that provide a way of organising data from multiple heterogeneous sources and have recently begun to be used in drug discovery. This project will develop graph theory techniques for extracting information-rich feature vectors from knowledge graphs for use with machine learning methods. The aim will be to provide more accurate predictions of the physicochemical and biochemical properties of small molecules.