  Recent advances in molecular machine learning, especially deep neural
networks such as Graph Neural Networks (GNNs) for predicting structure activity
relationships (SAR) have shown tremendous potential in computer-aided drug
discovery. However, the applicability of such deep neural networks are limited
by the requirement of large amounts of training data. In order to cope with
limited training data for a target task, transfer learning for SAR modeling has
been recently adopted to leverage information from data of related tasks. In
this work, in contrast to the popular parameter-based transfer learning such as
pretraining, we develop novel deep transfer learning methods TAc and TAc-fc to
leverage source domain data and transfer useful information to the target
domain. TAc learns to generate effective molecular features that can generalize
well from one domain to another, and increase the classification performance in
the target domain. Additionally, TAc-fc extends TAc by incorporating novel
components to selectively learn feature-wise and compound-wise transferability.
We used the bioassay screening data from PubChem, and identified 120 pairs of
bioassays such that the active compounds in each pair are more similar to each
other compared to its inactive compounds. Our experiments clearly demonstrate
that TAc achieves significant improvement over all baselines across a large
number of target tasks. Furthermore, although TAc-fc achieves slightly worse
ROC-AUC on average compared to TAc, TAc-fc still achieves the best performance
on more tasks in terms of PR-AUC and F1 compared to other methods. In summary,
TAc-fc is also found to be a strong model with competitive or even better
performance than TAc on a notable number of target tasks.
