  Motivation: Drug discovery demands rapid quantification of compound-protein
interaction (CPI). However, there is a lack of methods that can predict
compound-protein affinity from sequences alone with high applicability,
accuracy, and interpretability.
  Results: We present a seamless integration of domain knowledges and
learning-based approaches. Under novel representations of
structurally-annotated protein sequences, a semi-supervised deep learning model
that unifies recurrent and convolutional neural networks has been proposed to
exploit both unlabeled and labeled data, for jointly encoding molecular
representations and predicting affinities. Our representations and models
outperform conventional options in achieving relative error in IC$_{50}$ within
5-fold for test cases and 20-fold for protein classes not included for
training. Performances for new protein classes with few labeled data are
further improved by transfer learning. Furthermore, separate and joint
attention mechanisms are developed and embedded to our model to add to its
interpretability, as illustrated in case studies for predicting and explaining
selective drug-target interactions. Lastly, alternative representations using
protein sequences or compound graphs and a unified RNN/GCNN-CNN model using
graph CNN (GCNN) are also explored to reveal algorithmic challenges ahead.
  Availability: Data and source codes are available at
https://github.com/Shen-Lab/DeepAffinity
  Supplementary Information: Supplementary data are available at
http://shen-lab.github.io/deep-affinity-bioinf18-supp-rev.pdf
