DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks

Abstract 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…

• NI
  National Institutes of Health

  Award: R35GM124952
• DA
  Defense Advanced Research Projects Agency

  Award: FA8750-18-2-0027
• HP
  High Performance Research Computing, Texas A and M University
• NI
  National Institute of General Medical Sciences