Primer on Graph Machine Learning

We review basic techniques of machine learning methods for graph structured data, which are used for drug development. We introduce graph kernels, which measure the similarity between graphs and can be combined with any kernel methods, and graph neural networks, which are neural networks designed for machine learning such as classification and regression on graphs. We also review reinforcement learning, which enables us to efficiently search desirable drugs in enormous search space.