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 in achieving relative error in IC50 within 5-fold for test cases and 20-fo

SUBMITTER: Karimi M 

PROVIDER: S-EPMC6748780 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature