Multi-task learning for molecular property prediction is becoming increasingly important in drug discovery. However, in contrast to other domains, the performance of multi-task learning in drug discovery is still not satisfying as the number of labeled data for each task is too limited, which calls for additional data to complement the data scarcity. In this paper, we study multi-task learning for molecule property prediction in a different setting, where a relation graph between different tasks is available. We first extract a dataset including ~400 tasks as well as a relation graph between different tasks. Then we systematically investigate modeling the relation between different tasks: (1) in the latent space by learning effective task representations on the task relation graph; (2) and in the output space via structured prediction methods (e.g., energy-based methods). Empirical results prove the effectiveness of our proposed approaches.