  How to accurately predict the properties of molecules is an essential problem
in AI-driven drug discovery, which generally requires a large amount of
annotation for training deep learning models. Annotating molecules, however, is
quite costly because it requires lab experiments conducted by experts. To
reduce annotation cost, deep Active Learning (AL) methods are developed to
select only the most representative and informative data for annotating.
However, existing best deep AL methods are mostly developed for a single type
of learning task (e.g., single-label classification), and hence may not perform
well in molecular property prediction that involves various task types. In this
paper, we propose a Task-type-generic active learning framework (termed Tyger)
that is able to handle different types of learning tasks in a unified manner.
The key is to learn a chemically-meaningful embedding space and perform active
selection fully based on the embeddings, instead of relying on
task-type-specific heuristics (e.g., class-wise prediction probability) as done
in existing works. Specifically, for learning the embedding space, we
instantiate a querying module that learns to translate molecule graphs into
corresponding SMILES strings. Furthermore, to ensure that samples selected from
the space are both representative and informative, we propose to shape the
embedding space by two learning objectives, one based on domain knowledge and
the other leveraging feedback from the task learner (i.e., model that performs
the learning task at hand). We conduct extensive experiments on benchmark
datasets of different task types. Experimental results show that Tyger
consistently achieves high AL performance on molecular property prediction,
outperforming baselines by a large margin. We also perform ablative experiments
to verify the effectiveness of each component in Tyger.
