  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 molecular property prediction in a novel setting,
where a relation graph between tasks is available. We first construct a dataset
(ChEMBL-STRING) including around 400 tasks as well as a task relation graph.
Then to better utilize such relation graph, we propose a method called SGNN-EBM
to systematically investigate the structured task modeling from two
perspectives. (1) In the \emph{latent} space, we model the task representations
by applying a state graph neural network (SGNN) on the relation graph. (2) In
the \emph{output} space, we employ structured prediction with the energy-based
model (EBM), which can be efficiently trained through noise-contrastive
estimation (NCE) approach. Empirical results justify the effectiveness of
SGNN-EBM. Code is available on https://github.com/chao1224/SGNN-EBM.
